National Environmental Research Institute University of Aarhus . Denmark NERI Technical Report No. 632, 2007 Denmark’s NationaI Inventory Report 2007 Emission Inventories – Submitted under the United Nations Framework Convention on Climate Change, 1990-2005
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National Environmental Research InstituteUniversity of Aarhus . Denmark
Lars VesterdalForest and Landscape, University of Copenhagen
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Series title and no.: NERI Technical Report No. 632
Title: Denmark’s National Inventory Report 2007 Subtitle: Emission Inventories - Submitted under the United Nations Framework Convention on Climate
Change, 1990-2005
Authors: Jytte Boll Illerup1, Erik Lyck1, Ole-Kenneth Nielsen1, Mette Hjorth Mikkelsen1, Leif Hoffmann1, Steen Gyldenkærne1, Malene Nielsen1, Morten Winther1, Patrik Fauser1, Marianne Thomsen1, Peter Borgen Sørensen2, Lars Vesterdal3
Departments: 1) Department of Policy Analysis, National Environmental Research Institute, University of Aarhus 2) Department of Terrestrial Ecology, National Environmental Research Institute, University of Aarhus 3) Department of Forest and Landscape, University of Copenhagen Publisher: National Environmental Research Institute
University of Aarhus - Denmark URL: http://www.neri.dk
Year of publication: October 2007 Editing completed: April 2007 Referee: Hanne Bach Financial support: No external financial support
Please cite as: Illerup, J.B., Lyck, E., Nielsen, O.-K., Mikkelsen, M.H., Hoffmann, L., Gyldenkærne, S., Nielsen, M., Winther, M., Fauser, P., Thomsen, M., Sørensen, P.B. & Vesterdal, L., 2007: Denmark’s National Inventory Report 2007 - Emission Inventories - Submitted under the United Nations Framework Convention on Climate Change, 1990-2005. National Environmental Research Institute, University of Aarhus. 642 pp. – NERI Technical Report no. 632. http://www.dmu.dk/Pub/FR632
Reproduction permitted provided the source is explicitly acknowledged
Abstract: This report is Denmark’s National Inventory Report reported to the Conference of the Parties under the United Nations Framework Convention on Climate Change (UNFCCC) due by 15 April 2007. The report contains information on Denmark’s inventories for all years’ from 1990 to 2005 for CO2, CH4, N2O, HFCs, PFCs and SF6, CO, NMVOC, SO2.
Internet version: The report is available in electronic format (pdf) at NERI's website http://www.dmu.dk/Pub/FR632.pdf
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�-��!������!������� ES.1. Background information on greenhouse gas inventories and climate change 7 ES.2. Summary of national emission and removal trends 8 ES.3. Overview of source and sink category emission estimates and trends 9 ES.4. Other information 10
����.���(�*/ S.1. Baggrund for opgørelse af drivhusgasemissioner og klimaændringer 14 S.2. Udviklingen i emissioner og optag 15 S.3. Oversigt over emissionskilder 16 S.4. Andre informationer 17
* ����#!������� 1.1 Background information on greenhouse gas inventories and climate change
20 1.2 A description of the institutional arrangement for inventory preparation 22 1.3 Brief description of the process of inventory preparation. Data collection and
processing, data storage and archiving 23 1.4 Brief general description of methodologies and data sources used 25 1.5 Brief description of key source categories 34 1.6 Information on QA/QC plan including verification and treatment of confidential
issues where relevant 34 1.7 General uncertainty evaluation, including data on the overall uncertainty for
the inventory totals 49 1.8 General assessment of the completeness 52 References 52
� ���#����0����!���0������������,, 2.1 Description and interpretation of emission trends for aggregated greenhouse
gas emissions 55 2.2 Description and interpretation of emission trends by gas 55 2.3 Description and interpretation of emission trends by source 58 2.4 Description and interpretation of emission trends for indirect greenhouse
gases and SO2 59
� ���(��1'�%��������*2��� 3.1 Overview of the sector 62 3.2 Stationary combustion (CRF sector 1A1, 1A2 and 1A4) 65 3.3 Transport and other mobile sources (CRF sector 1A2, 1A3, 1A4 and 1A5) 95 References for Chapter 3.3 159 3.4 Additional information, CRF sector 1A Fuel combustion 162 3.5 Fugitive emissions (CRF sector 1B) 163 References for Chapters 3.2, 3.4 and 3.5 174
/ �#!����� �����������1'�%� �������2�*�� 4.1 Overview of the sector 176 4.2 Mineral products (2A) 178 4.3 Chemical industry (2B) 183 4.4 Metal production (2C) 185 4.5 Production of Halocarbons and SF6 (2E) 186 4.6 Metal Production (2C) and Consumption of Halocarbons and SF6 (2F) 186 4.7 Uncertainty 193
4.8 Quality assurance/quality control (QA/QC) 194 References 199
, � ������#���������#!���!���1'�%� �������2���� 5.1 Overview of the sector 202 5.2 Paint application (CRF Sector 3A), Degreasing and dry cleaning (CRF Sector
3B), Chemical products, Manufacture and processing (CRF Sector 3C) and Other (CRF Sector 3D) 202
*� ���� �! �������#���������������+ 10.1 Explanations and justifications for recalculations 339 10.2 Implications for emission levels 341 10.3 Implications for emission trends, including time series consistency 342 10.4 Recalculations, including those in response to the review process, and
planned improvements to the inventory (e.g. institutional arrangements, inventory preparations 342
������������This report is Denmark’s National Inventory Report (NIR), for submis-sion to the United Nations Framework Convention on Climate Change (UNFCCC), for 15 April 2007. The report contains information on Den-mark’s inventories for all years from 1990 to 2005. The structure of the report is in accordance with the UNFCCC guidelines on reporting and review. The report includes detailed information on the inventories for all years, from the base year to the year of the current annual inventory submission, in order to ensure transparency.
The annual emission inventory for Denmark from 1990 to 2005 is re-ported in the Common Reporting Format (CRF). The CRF spreadsheets contain data on emissions, activity data and implied emission factors for each year. Emission trends are given for each greenhouse gas and for to-tal greenhouse gas emissions in CO2 equivalents.
The issues addressed in this report are: Trends in greenhouse gas emis-sions, description of each emission category of the CRF, uncertainty es-timates, explanations on recalculations, planned improvements and pro-cedure for quality assurance and control.
The NIR is available to the public on the National Environmental Re-search Institute’s homepage:
http://www.dmu.dk/International/Publications/
(search for "National Inventory Report 2007")
and the CRF tables are available at the Eionet web site:
This report does not contain the full set of CRF Tables. Only the trend ta-bles, Tables 10.1-5 of the CRF format, are included in Annex 9.
Concerning figures, please note that figures in the CRF tables (and An-nex 9) are in the Danish notation which is “,” (comma) for decimal sign and “.” (Full stop) to divide thousands. In the report (except where tables are taken from the CRF as “pictures” as Annex 9) English notation is used: “.” (Full stop) for decimal sign and (mostly) space for division of thousands. The English notation for division of thousand as “,” (comma) is not use due to the risk to be misinterpreted in Danish.
8
�� ��������� � �����The National Environmental Research Institute (NERI), University of Aarhus, is responsible for the annual preparation and submission to the UNFCCC and the EU of the National Inventory Report and the GHG in-ventories in the Common Reporting Format, in accordance with the UNFCCC guidelines. NERI is also the body designated with overall re-sponsibility for the national inventory under the Kyoto Protocol. The work concerning the annual greenhouse emissions inventory is carried out in cooperation with Danish ministries, research institutes, organisa-tions and companies.
������� ���� � �The greenhouse gases reported under the Climate Convention are:
• Carbon dioxide CO2 • Methane CH4 • Nitrous Oxide N2O • Hydrofluorocarbons HFCs • Perfluorocarbons PFCs • Sulphur hexafluoride SF6 The global warming potential (GWP) for various gases has been defined as the warming effect over a given time of a given weight of a specific substance relative to the same weight of CO2. The purpose of this meas-ure is to be able to compare and integrate the effects of individual sub-stances on the global climate. Typical lifetimes in the atmosphere of sub-stances are very different, e.g. approximately for CH4 and N2O, 12 and 120 years respectively. So the time perspective clearly plays a decisive role. The lifetime chosen is typically 100 years. The effect of the various greenhouse gases can, then, be converted into the equivalent quantity of CO2, i.e. the quantity of CO2 giving the same effect in absorbing solar ra-diation. According to the IPCC and their Second Assessment Report, which UNFCCC has decided to use as reference, the global warming po-tentials for a 100-year time horizon are:
• CO2: 1 • Methane (CH4): 21 • Nitrous oxide (N2O): 310 Based on weight and a 100-year period, methane is thus 21 times more powerful a greenhouse gas than CO2, and N2O is 310 times more power-ful than CO2. Some of the other greenhouse gases (hydrofluorocarbons, perfluorocarbons and sulphur hexafluoride) have considerably higher global warming potentials. For example, sulphur hexafluoride has a global warming potential of 23,900. The values for global warming po-tential used in this report are those prescribed by UNFCCC.
���������� �������������������� ������� ���
������� ���� ���� �� �The greenhouse gas emissions are estimated according to the IPCC guidelines and are aggregated into seven main sectors. The greenhouse gases include CO2, CH4, N2O, HFCs, PFCs and SF6. Figure ES.1 shows the estimated total greenhouse gas emissions in CO2 equivalents from
9
1990 to 2005. The emissions are not corrected for electricity trade or tem-perature variations. CO2 is the most important greenhouse gas, followed by N2O and CH4 in relative importance. The contribution to national to-tals from HFCs, PFCs and SF6 is approximately 1%. Stationary combus-tion plants, transport and agriculture represent the largest sources. The net CO2 removal by forestry and soil (Land Use and Land Use Change and Forestry (LULUCF)) is in the region of 2 % of the total emission in CO2 equivalents in 2005. The national total greenhouse gas emission in CO2 equivalents without LUCF has decreased by 7 % from 1990 to 2005 and by 10 % with LULUCF.
��������� Greenhouse gas emissions in CO2 equivalents distributed on main sectors for 2005 and time-series for 1990 to 2005.
�������The largest source of the emission of CO2 is the energy sector, which in-cludes the combustion of fossil fuels such as oil, coal and natural gas. Public power and district heating plants contribute with 44 % of the emissions. Approximately 26 % come from the transport sector. The CO2 emission decreased by approximately 7 % from 2004 to 2005. A relatively large fluctuation in the emission time-series from 1990 to 2005 is due to inter-country electricity trade. Thus, high emissions in 1991, 1996 and 2003 reflect electricity export and the low emissions in 1990 and 2005 were due to import of electricity in these years. The increasing emission of CH4 is due to increasing use of gas engines in the decentralised co-generation plants. The CO2 emission from the transport sector has in-creased by 26 % since 1990, mainly due to increasing road traffic.
������������The agricultural sector contributes with 16 % of the total greenhouse gas emission in CO2-equivalents and is one of the most important sectors re-garding the emissions of N2O and CH4. In 2005, the contributions to the total emissions of N2O and CH4 were 89 % and 65 %, respectively. The main reason for a fall of approximately 31% in the emission of N2O from 1990 to 2005 is legislative demand for an improved utilisation of nitrogen in manure. This result in less nitrogen excreted per livestock unit pro-duced and a considerable reduction in the use of fertilisers. From 1990, the emission of CH4 from enteric fermentation has decreased due to de-creasing numbers of cattle. However, the emission from manure man-
Energy andtransportation
78,3%
Agriculture15,5%
Solvents0,2%
Industrialprocesses
3,9%
Waste2,1%
0
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CO
2 eq
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0 to
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) CO2
CH4
N2O
HFC’s,PFC’s,SF6Total
10
agement has increased due to changes in stable management systems towards an increase in slurry-based systems. Altogether, the emission of CH4 for the agricultural sector has decreased by 9 % from 1990 to 2005.
���� ��������� � �The emissions from industrial processes – i.e. emissions from processes other than fuel combustion, amount to 4 % of total emissions in CO2-equivalents. The main sources are cement production, refrigeration, foam blowing and calcination of limestone. The CO2 emission from ce-ment production – which is the largest source contributing with about 3 % of the national total – increased by 65 % from 1990 to 2005. The second largest source has been N2O from the production of nitric acid. However, the production of nitric acid/fertiliser creased in 2004 and therefore the emission of N2O also creased.
The emission of HFCs, PFCs and SF6 has, since 1995 until 2005, increased by 158 %, largely due to the increasing emission of HFCs. The use of HFCs, and especially HFC-134a, has increased several fold, so HFCs have become dominant F-gases, contributing 67% to the F-gas total in 1995, rising to 96% in 2005. HFC-134a is mainly used as a refrigerant. However, the use of HFC-134a is now stable. This is due to Danish legis-lation, which, in 2007, forbids new HFC-based refrigerant stationary sys-tems. Running counter to this trend, however, is the increasing use of air conditioning systems among mobile systems.
������ ������������ ���������������� �������������The LULUCF sector is generally a net sink. In 2005 it has been estimated to be a net sink equivalent to 2% of the total emission. This is lower to previous years due to stormfelling in the forests in 2005 reducing the net sink in forests from normally 3500 Gg CO2/yr to 1852 Gg CO2/yr. In cropland a net sink has been estimated of 308 Gg CO2 with the organic soils as source and the mineral cropland as net sink. The emission esti-mate from cropland is calculated with a dynamic model taking into ac-count harvest yields and actual temperatures and thus may therefore fluctuate between years. 2005 was an average year and the emission from cropland is therefore an average estimate. Only a small area with per-manent grassland is occurring in Denmark and has only little influence on the overall emission trend.
�� ���Waste disposal is the third largest source of the CH4 emission. The emis-sion has decreased by 21 % from 1990 to 2005, at which point the contri-bution from waste was 19 % of the total CH4 emission. This decrease is due to the increasing use of waste for power and heat production. Since all incinerated waste is used for power and heat production, the emis-sions are included in the 1A1a IPCC category. The CH4 emission from wastewater handling amounts to around 5 % of the total CH4 emission.
��� ����� ���� ������
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A plan for Quality Assurance (QA) and Quality Control (QC) in green-house gas emission inventories is included in the report. The plan is in
11
accordance with the guidelines provided by the UNFCCC (Good Practice Guidance and Uncertainty Management in National Greenhouse Gas In-ventories and Guidelines for National Systems). ISO 9000 standards are also used as an important input for the plan.
The plan comprises a framework for documenting and reporting emis-sions in a way that emphasises transparency, consistency, comparability, completeness and accuracy. To fulfil these high criteria, the data struc-ture describes the pathway, from the collection of raw data to data com-pilation and modelling and final reporting.
As part of the Quality Assurance (QA) activities, emission inventory sec-tor reports have been prepared and sent to national experts, not involved in the inventory development, for review. To date, the reviews have been completed for the stationary combustion plants sector, the transport sec-tor and the agriculture sector. In order to evaluate the Danish emission inventories, a project where emission levels and emission factors are compared with those in other countries has been performed.
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The Danish greenhouse gas emission inventory, which was due 15 April 2007, includes all sources identified by the revised IPPC guidelines ex-cept the following:
Agriculture: The methane conversion factor in relation to the enteric fermentation for poultry and fur farming is not estimated. There is no default value recommended by the IPCC. However, this emission is seen as non-significant compared with the total emission from enteric fermen-tation.
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The main improvements of the inventories are:
��� ��
�������������� ����For stationary combustion plants the emission estimates have been up-dated according to latest energy statistics published by the Danish En-ergy Authority. The update includes the years 1990-2004. This is the main reason for the changes in this sector. However changed fuel type aggregation also caused imperceptible changes.
The distribution of emissions from the industrial sector, 1A2 was up-dated based on new information from Statistics Denmark and the Danish Energy Authority. The total emission from category 1A2 was not affected only the distribution between the sub-sectors 1A2a-1A2f.
Harmonisation of the GHG inventory and the information compiled for the European Emission Trading System (ETS) is on-going.
����� ���� �The biggest changes for CO2 are seen for off-road vehicles in the agricul-ture sector, where updated stock information for tractors and harvesters
12
(2001-2004) have resulted in increasing estimated fuel consumption and emissions.
Minor changes are:
1) The estimated consumotion of fuel oil from the fishery sector in the national energy statistics has been moved to the national sea trans-port category, resulting in emission changes for 1990-2004.
2) A minor amount of diesel oil fuel use has been subtracted from the fishery sector, in order to correct an error in last year’s submission for 1990-2004.
&���� �No methodological changes have been introduced in the 2005 GHG in-ventory. Harmonisation of the GHG inventory and the information compiled for the ETS is on-going.
�������A survey based on new methodologies results in new NMVOC emission estimates. Revisions have been made regarding use of pentane and sty-rene in the plastic industry, use and emission factors of glycolethers, use and emission factor of tertrachloroethylene and reassignment of some product groups from degreasing to paints.
'� ������ �Small changes in the emission estimates for the agricultural sector have taken place. These changes reflect increased emissions from years 1990-2004 by less than 1 %. There is no change in the calculation methodology. Based on the expert review team request, the feed consumption for dairy cattle 1990 – 1994 has been interpolated, in order to remove the time-series inconsistency. Another change is due to updated normdata for ni-trogen excretion in 2003 and new data for export of living poultry from 1994.
(���The methodology for CH4-emissions from solid waste disposal sites has been slightly changed following a suggestion by the review team. The point was in the decay model to change the use of the oxidation factor, so that the subtraction of CH4 due to oxidation was done after the sub-traction due to recovered CH4. The change has resulted in an increase in yearly CH4 emission from solid waste disposals for the time-series up to maximum of 2 %.
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������!���� ���������"������ �A small recalculation has been made for the area converted from crop-land and grassland to wetlands. The total area affected by this is less than 0.02 % of the Danish agricultural area. The influence on the emis-sion estimate is almost zero.
The new report from UNFCCC has made it possible to include CH4 emissions from wetlands, which was not possible earlier. Drainage of wetlands with the aim of peat extraction reduces the emissions from these areas. The total area with peat extraction is 887 ha. For all years are included a reduced CH4 emission due to the drainage. A standard emis-
13
sion factor of 20 kg/CH4/ha/yr is used. The effect of this on the total LULUCF sector is < 0.1 %.
.����������For the ��������� ����� ��� � ��������� ������������������ ������������������� ������ ���� ��������� �������,� the general impact of the improvements and recalculations performed is small and the changes for the whole time-series are between -0.02 % and +0.18 %. Therefore, the implications of the recalculations on the level and on the trend, 1990-2004, of this national total are small.
For the ��������� ����� ��� � ��������� ���������� ����� ���������������������������������������������,�the general impact of the ��� �� �������� is rather small, although the impact is larger than without LULUCF due to recalculations in the LULUCF sector for 2003 and 2004. The differences vary between –1.01 % and +0.14 %. These differences re-fer to recalculated estimates, with major changes in the LULUCF for those years.
#����������Denne rapport er Danmarks årlige rapport om drivhusgasopgørelser sendt til FN’s konvention om klimaændringer (UNFCCC) den 15. april 2007. Rapporten indeholder oplysninger om Danmarks opgørelser fra 1990 til 2005. Rapporten er struktureret som angivet i IPCC’s retningsli-nier for rapportering og evalueringer af drivhusgasopgørelser. For at sikre at opgørelserne er gennemskuelige indeholder rapporten detaljere-de oplysninger om opgørelsesmetoder og baggrundsdata for alle årene fra basisåret og frem til det seneste rapporterede år.
Den årlige emissionsopgørelse for Danmark for årene 1990 til 2005 er rapporteret i det format (CRF) som Klimakonventionen foreskriver. CRF-tabellerne indeholder oplysninger om emissioner, aktivitetsdata og emis-sionsfaktorer for hvert år, emissionsudvikling for de enkelte drivhusgas-ser samt den totale drivhusgasemission i CO2-ækvivalenter.
Følgende emner er beskrevet i rapporten: Udviklingen i drivhusgasemis-sionerne, de forskellige emissionskategorier i CRF-fomatet, usikkerhe-der, rekalkulationer, planlagte forbedringer og procedure for kvalitets-sikring og – kontrol.
Rapporten er tilgængelig på DMU’s hjemmeside http://www.dmu.d-k/International/Publications/ (søg efter "National Inventory Report 2007") og CRF tabellerne er tilgængelig på Eionet web site: http://cdr.e-ionet.europa.eu/dk/Air_Emission_Inventories/Submission_UNFCCC
�� $��������� ������Danmarks Miljøundersøgelser (DMU) under Aarhus Universitet, er an-svarlig for udarbejdelse af de danske drivhusgasemissioner og den årlige rapportering til UNFCCC og kontaktpunktet for Danmarks nationale sy-stem til drivhusgasopgørelser under Kyoto-protokollen. DMU deltager desuden i arbejdet i UNFCCC regi, hvor retningsliner for rapportering diskuteres og vedtages og i EU’s moniteringsmekanisme for opgørelse af drivhusgasser, hvor retningslinier for rapportering til EU reguleres. Ar-bejdet med de årlige opgørelser udføres i samarbejde med andre danske ministerier, forskningsinstitutioner, organisationer og private virksom-heder.
%��$�� �� ���Til Klimakonventionen rapporteres følgende drivhusgasser:
• Svovlhexafluorid SF6 Det globale opvarmningspotentiale, på engelsk Global Warming Poten-tial (GWP), udtrykker klimapåvirkningen over en nærmere angivet tid af en vægtenhed af en given drivhusgas relativt til samme vægtenhed af CO2. Drivhusgasser har forskellige karakteristiske levetider i atmosfæ-ren, således for metan ca. 12 år og for lattergas ca. 120 år. Derfor spiller tidshorisonten en afgørende rolle for størrelsen af GWP. Typisk vælger man 100 år. Herefter kan man omregne effekten af de forskellige driv-husgasser til en ækvivalent mængde kuldioxid, dvs. til den mængde kuldioxid der vil give samme klimapåvirkning. Til rapporteringen til klimakonventionen er vedtaget at anvende GWP-værdier for en 100-årig tidshorisont, som ifølge IPCC’s anden vurderingsrapport er:
• Kuldioxid, CO2: 1 • Metan, CH4: 21 • Lattergas, N2O: 310 Regnet efter vægt og over en 100-årig periode er metan således ca. 21 og lattergas ca. 310 gange så effektive drivhusgasser som kuldioxid. Nogle af de øvrige drivhusgasser (HFC, PFC, SF6) har væsentlig højere GWP-værdier, som fx SF6, der har en beregnet værdi på 23.900. I denne rapport er anvendt de GWP-værdier som UNFCCC har anbefalet.
����*������������������ ���$���
1 ������������� De danske emissionsopgørelser følger metoderne beskrevet i IPCC’s ret-ningslinier og er aggregerede i syv overordnede kategorier. Drivhusgas-serne omfatter CO2, CH4, N2O, HFC’er, PFC’er og SF6. Figur S.1 viser de estimerede totale drivhusgasemissioner i CO2-ækvivalenter for perioden 1990 til 2005. Emissionerne er ikke korrigerede for handel med elektrici-tet med andre lande og temperatursvingninger fra år til år. CO2 er den vigtigste drivhusgas efterfulgt af N2O og CH4, mens HFC’er, PFC’er og SF6 kun udgør ca. 1 % af de totale emissioner. Stationære forbrændings-anlæg, transport og landbrug er de største kilder. Netto-CO2-optaget af skov og jorde (Land Use Land Use Change and Forestry) var ca. 2 % af de totale emissioner i CO2-ækvivalenter i 2005. De nationale totale driv-husgasemissioner i CO2-ækvivalenter er faldet med 7 % fra 1990 til 2005 hvis netto-bidraget fra skovenes og jordenes udledninger og optag af CO2 ikke indregnes og med 10 % hvis de indregnes.
0
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2 æ
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Total udenLUCF
������� Danske drivhusgasemissioner i CO2-ækvivalenter for hovedsektorer for 2005 og tidsserier for 1990-2005.
Opløsnings- midler 0,2%
Landbrug 15,5%
Industrielle processer
3,9%
Affald 2,1%
Energi og transport 78,3%
16
������� ������ �����������
�������Udledningen af CO2 stammer altovervejende fra forbrænding af kul, olie og naturgas på kraftværker samt i beboelsesejendomme og industri. Kraft- og fjernvarmeværker bidrager med 44 % af emissionerne og om-kring 26 % stammer fra transportsektoren. CO2-emissionen faldt med omkring 7 % fra 2004 til 2005. De relative store udsving i emissionerne fra år til år skyldes handel med elektricitet med andre lande, herunder særligt de nordiske. De høje emissioner i 1991, 1994, 1996 og 2003 er et resultat af stor eksport af elektricitet, mens de lave emissioner i 1990 og 2005 skyldes import af elektricitet. Udledningen af metan fra energipro-duktion har været stigende på grund af øget anvendelse af gasmotorer, som har et stort metan-udslip i forhold til andre forbrændingsteknologi-er. Transportsektorens CO2-emissioner er steget med ca. 26 % siden 1990 hovedsagelig på grund af voksende vejtrafik.
���������Landbrugssektoren bidrager med 16 % af de totale drivhusgasser i CO2-ækvivalenter og er den vigtigste kilde hvad angår emissioner af N2O og CH4. I 2005 var bidragene til de totale emissioner af N2O og CH4 hen-holdsvis 89 % og 65 %. Fra 1990 ses et fald på 31 % i N2O-emissionen fra landbrug. Det skyldes mindre brug af handelsgødning og bedre udnyt-telse af husdyrgødningen, hvilket resulterer i mindre emissioner pr. pro-ducerede dyreenhed. Emissionerne fra husdyrenes fordøjelsessystem er faldet fra 1990 til 2005 grundet et faldende antal kvæg. På den anden side har en stigende andel af gyllebaserede staldsystemer bevirket at emissi-onerne fra husdyrgødning er steget. I alt er CH4 emissionerne fra land-brugssektoren faldet med 9 % fra 1990 til 2005.
���� ����������� ���Emissionerne fra industrielle processer – hvilket vil sige andre processer end forbrændingsprocesser – udgør 4 % af de totale danske drivhusgas-emissioner. De vigtigste kilder er cementproduktion, kølesystemer, op-skumning af plast og kalcinering af kalksten. CO2-emissionen fra ce-mentproduktion - som er den største kilde - bidrager med ca. 3 % af de totale emissioner i 2004 og stigningen fra 1990 til 2005 var 65 %. Den an-den største kilde har tidligere været lattergas fra produktion af salpeter-syre. Produktionen af salpetersyre stoppede i midten af 2004, hvilket be-tyder at lattergasemissionen er nul for denne kilde i 2005.
Emissionerne af HFC’er, PFC’er og SF6 er siden 1995 og indtil 2005 steget med 158 % hovedsageligt på grund af stigende emissioner af HFC’erne. Anvendelsen af HFC’erne, og specielt HFC-134a, er steget kraftigt, hvil-ket har betydet at andelen af HFC’er af de totale F-gasser steg fra 67 % i 1995 og til 96 % i 2005. HFC’erne anvendes primært inden for køleindu-strien. Anvendelsen er dog nu stagnerende, som et resultat af dansk lov-givning, der forbyder anvendelsen af nye HFC-baserede stationære køle-systemer fra 2007. I modsætning til denne udvikling ses et stigende brug af airconditionsystemer i køretøjer.
�������$����� �����������Arealanvendelse omfatter udslip og bindinger fra skov- og landbrugs-arealet. Denne sektor binder generelt CO2. I 2005 er sektoren estimeret til at binde ca. 2 % af det samlede udslip af drivhusgasser. Dette er mindre
17
end tidligere år på grund af stormfaldet i de danske skove i 2005, som har reduceret bindingen i skov fra normalt 3500 Gg CO2/år til 1852 Gg CO2/år. Landbrugsarealet er estimeret til at have en nettobinding på 308 Gg CO2. Her har de organiske jorde et nettoudslip af CO2 mens mineral-jordene har en nettobinding. Bindingen i mineraljorde er beregnet med en dynamisk model som tager hensyn til det årlige høstudbytte og de ak-tuelle temperaturer, hvorfor den vil variere mellem år. 2005 var et gen-nemsnitsår og bindingen i landbrugsarealet kan derfor ses som et gen-nemsnit. I Danmark findes der kun et meget lille areal med permanente græsmarker, hvorfor det kun har en lille indflydelse på den samlede ud-vikling i drivhusgasudledningen.
�&&����Lossepladser er den tredjestørste kilde til CH4 emissioner. Emissionen er faldet med 21 % fra 1990 til 2005, hvor andelen var 19 % af de totale CH4 emissioner. Faldet skyldes stigende anvendelse af affald til produktion af elektricitet og varme. Da al affaldsforbrænding bruges til produktion af elektricitet og varme, er emissionerne inkluderet i IPCC-kategorien 1A1a, der omfatter kraft- og fjernvarmeværker. Emissionerne fra spilde-vandsanlæg udgør omkring 5 % af de totale CH4-emissioner.
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Rapporten indeholder en plan for kvalitetssikring og -kontrol af emissi-onsopgørelserne. Kvalitetsplanen bygger på IPCC’s retningslinier og ISO 9000 standarderne. Planen skaber rammer for dokumentering og rappor-tering af emissionerne, så opgørelserne er gennemskuelige, konsistente, sammenlignelige, komplette og nøjagtige. For at opfylde disse kriterier, understøtter datastrukturen arbejdsgangen fra indsamling af data til sammenstilling, modellering og til sidst rapportering af data.
Som en del af kvalitetssikringen, er der for alle emissionskilder udarbej-det rapporter, der detaljeret beskriver og dokumenterer anvendte data og beregningsmetoder. Disse rapporter evalueres af personer uden for DMU, der har høj faglig ekspertise indenfor det pågældende område, men som ikke direkte er involveret i arbejdet med opgørelserne. Indtil nu er rapporter for stationære forbrændingsanlæg, transport og land-brug blevet evalueret. Desuden er der gennemført et projekt, hvor de danske opgørelsesmetoder, emissionsfaktorer og usikkerheder sammen-lignes med andre landes, for yderligere at verificere rigtigheden af opgø-relserne.
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De danske opgørelser af drivhusgasemissioner, som blev rapporteret den 15. april 2007 til UNFCCC, indeholder alle de kilder der er beskrevet i IPCC’s retningsliner undtagen:
Landbrug: Metankonverteringsfaktoren for emissioner fra kyllingers og pelsdyrs fordøjelsessystemer er ikke bestemt, og der findes ingen IPCC standardemissionsfaktor. Emissionerne fra disse dyrs fordøjelsessyste-
18
mer anses dog for at være forsvindende i forhold til de totale emissioner fra fordøjelsessystemer.
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De vigtigste forbedringer af opgørelserne er:
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������'��&���'������For stationær forbrænding er emissionsopgørelserne blevet opdateret i henhold til den seneste officielle energistatistik publiceret af Energisty-relsen. Opdateringen inkluderer årene 1990-2004. Denne opdatering er grundlaget for de fleste ændringer indenfor stationær forbrænding.
Emissionsfaktoren for NMVOC for forbrænding af træ i husholdninger er blevet opdateret for hele tidsserien baseret på nye danske beregninger.
Fordelingen af emissioner fra industriens energiforbrug, sektor 1A2, er opdateret i henhold til nye data fra Danmarks Statistik og Energistyrel-sen. Fordelingen påvirker ikke den totale emission for sektoren men kun fordelingen på undergrupperne 1A2a-1A2f.
�����(������For landbrug er bestandsdata for traktorer og mejetærskere opdaterede for årene 2001-2004, hvilket har medført en stigning i energiforbrug og emissioner. CO2–emissions stigningen modsvares dog af et tilsvarende fald i emissionerne for stationær forbrænding, da energiforbruget er re-duceret her.
Det antages, at der ikke anvendes tung olie indenfor fiskeri. Forbruget af tung olie medregnes derfor under sektoren national søtransport. Denne ændring har medført en ændring i energiforbrug og emissioner fra 1990-2004, for disse to sektorer.
En lille del af forbruget af dieselolie er fratrukket fiskerisektoren, hvor det var medregnet ved en fejl i sidste års indberetning for 1990-2004.
Samlet er CO2 ændringerne for sektoren landbrug/skovbrug/fiskeri på mellem -1 % og +3 % for 1990 til 2004.
CH4 emissionen fra vejtrafik er ændret, grundet anvendelse af nye emis-sionsfaktorer fra COPERT IV, samt nyt trafikarbejde fra det periodiske synsprogram og en lille korrektion af energiforbruget. Emissionsændrin-gerne ligger i intervallet -11 % til +12 %.
Grundet anvendelse af nye emissionsfaktorer fra COPERT IV og vejtra-fikdata er emissionerne for NOx, CO og NMVOC ændret. For de samme stoffer samt SO2 er der også ændringer for søfart og fiskeri pga. ændrede emissionsfaktorer og de førnævnte energiforbrugsforskydninger (forkla-ret under CO2).
���� ����Der er ikke introduceret metodiske ændringer i opgørelsen for 2005. Harmonisering af GHG-opgørelsen med emissionsdata indsamlet i for-bindelse med EU’s kvotesystem er igangværende.
19
)�* ���� �������En ny undersøgelse har resulteret i nye estimater for NMVOC-emissionen. Rekalkulationen omfatter brug af pentan og styren i plastik-industrien samt brug af og emissionsfaktorer for glycolethere og tertrachloroethylene. Desuden er der sket en omgruppering af visse pro-dukter fra affedtning til maling.
���������Mindre ændringer af emissionen fra landbrugssektoren har resulteret i en lidt højere emission for årene fra 1990 til 1994. Opdateringen betyder ændringer på mindre end 1 % af totalemissionen. Der er ikke foretaget ændringer i beregningsmetoden. For at imødekomme forespørgsel fra review teamet, er foder input for malkekvæg fra 1990 til 1994 interpole-ret. På denne måde undgås den markante stigning i IEF fra 1993 til 1994, som gjorde sig gældende i den forrige emissionsopgørelse. Det skal nævnes, at der er foretaget en opdatering af normtallene for 2003. End-videre er data for eksporteret levende fjerkræ blevet tilgængelig og ind-går i opgørelsen.
�&&����Der er foretaget en mindre metodeændring til beregning af metan fra lossepladser. Ændringen er en følge af bemærkninger fra review teamet, som mente, at oxidation af metan finder sted efter, at metan er opsamlet til energiformål. Ændringen har resulteret i en mindre stigning i den be-regnede årlige metan emission, for alle årene 1990-2004 mindre end 2 %.
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�&��*���!���' ��+������$+���+����En lille rekalkulation er foretaget for arealerne overgået til vådområder. Dette omfatter mindre en 0,02 % af det danske landbrugsareal. Effekten på emissionen er næsten nul.
Det nye afrapporteringsværktøj fra UNFCCC gør det nu muligt at ind-regne metan emissioner i LULUCF-sektoren, hvilket ikke var muligt tid-ligere. Dræning af vådområder med henblik på spagnumgravning redu-cerer metan emissionen fra disse arealer. Det totale høstede areal med spagnum er opgjort til 887 hektar. For alle årene er der nu indført en re-duceret emission af CH4 som følge af tørlægningen af arealerne. En stan-dard emissionsfaktor på 20 kg CH4/ha/år er anvendt. Effekten af dette på den samlede LULUCF sektor er <0,1 %.
.�����0�� ���� Ændringer i de danske totale drivhusgasemissioner (i CO2-ækvivalenter), uden medtagning af emissioner og optag fra jorde og skov, som følge af forbedringer og rekalkulationer, er små i forhold til sidste års rapportering. Ændringerne for hele tidsserien 1990 til 2004 lig-ger mellem -0.02 % og +0.18 %.
Ændringer i de danske totale drivhusgasemissioner (i CO2-ækvivalenter) er større, når emissioner og optag fra jorde og skov medtages. Det skyl-des rekalkulationer i LULUCF-sektoren for 2003 og 2004. Ændringerne i forhold til sidste rapportering er dog stadig forholdsvis små og ligger for hele tidsserien 1990 til 2004 mellem –1.01 % and +0.14 %.
+�����������This report is Denmark’s National Inventory Report (NIR) for submis-sion by 15 April 2007 to the United Nations Framework Convention on Climate Change (UNFCCC) and the European Union’s Greenhouse Gas Monitoring Mechanism. The report contains information on Denmark’s inventories for all years from 1990 to 2005. The structure of the report is in accordance with the UNFCCC guidelines on reporting and review (UNFCCC, 2002). The report includes detailed and complete information on the inventories for all years from the base year to the year of the cur-rent annual inventory submission, in order to ensure transparency.
The annual emission inventories for Denmark, from 1990 to 2005, are re-ported in the Common Reporting Format (CRF) as requested in the re-porting guidelines. The CRF-spreadsheets contain data on emissions, ac-tivity data and implied emission factors for each year. Emission trends are given for each greenhouse gas and for the total greenhouse gas emis-sions in CO2 equivalents. The complete sets of the CRF-files are available on the Eionet web site:
while this report contains the CRF Tables 10.1 to 10.5, only (refer Annex 9).
The issues addressed in this report are trends in greenhouse gas emis-sions, a description of each IPCC category, uncertainty estimates, recal-culations, planned improvements and procedures for quality assurance and control.
According to the instrument of ratification, the Danish government has ratified the UNFCCC on behalf of Denmark, Greenland and the Faroe Is-lands. Annex 6.1 contains total emissions for Denmark, Greenland and the Faroe Islands for 1990 to 2005. In Annex 6.2, information on the Greenland and the Faroe Islands inventories are given. Apart from An-nexes 6.1 and 6.2, the information in this report relates only to Denmark.
The NIR is available to the public on the homepage of the Danish Na-tional Environmental Research Institute (NERI).
http://www.dmu.dk/International/Publications/ (search for "National Inventory Report 2007").
!���������(�����The greenhouse gases reported under the Climate Convention are:
21
• Carbon dioxide CO2 • Methane CH4 • Nitrous Oxide N2O • Hydrofluorocarbons HFCs • Perfluorocarbons PFCs • Sulphur hexafluoride SF6 The main greenhouse gas responsible for the anthropogenic influence on the heat balance is CO2. The atmospheric concentration of CO2 has in-creased from 280 to 370 ppm (about 30%) since the pre-industrial era in the nineteenth century (IPCC, Third Assessment Report). The main cause is the use of fossil fuels, but changing land use, including forest clear-ance, has also been a significant factor. Concentrations of the greenhouse gases methane and N2O, which are very much linked to agricultural production, have increased by 150% and 16%, respectively (IPCC, Third Assessment Report). Changes in the concentrations of greenhouse gases are not related in simple terms to the effect on the heat balance, however. The various gases absorb radiation at different wavelengths and with different efficiency. This must be considered in assessing the effects of changes in the concentrations of various gases. Furthermore, the lifetime of the gases in the atmosphere needs to be taken into account – the longer they remain in the atmosphere, the greater the overall effect. The global warming potential (GWP) for various gases has been defined as the warming effect over a given time of a given weight of a specific sub-stance relative to the same weight of CO2. The purpose of this measure is to be able to compare and integrate the effects of individual substances on the global climate. Typical lifetimes in the atmosphere of substances are very different, e.g. approximaty for CH4 and N2O, 12 and 120 years respectively. So the time perspective clearly plays a decisive role. The lifetime chosen is typically 100 years. The effect of the various green-house gases can, then, be converted into the equivalent quantity of CO2, i.e. the quantity of CO2 giving the same effect in absorbing solar radia-tion. According to the IPCC and their Second Assessment Report, which UNFCCC has decided to use as reference, the global warming potentials for a 100-year time horizon are:
• CO2: 1 • Methane (CH4): 21 • Nitrous oxide (N2O): 310 Based on weight and a 100-year period, methane is thus 21 times more powerful a greenhouse gas than CO2, and N2O is 310 times more power-ful. Some of the other greenhouse gases (hydrofluorocarbons, perfluoro-carbons and sulphur hexafluoride) have considerably higher global warming potential values. For example, sulphur hexafluoride has a global warming potential of 23,900.
'���.�������.��1���������"�����������%�������At the United Nations Conference on Environment and Development in Rio de Janeiro in June 1992, more than 150 countries signed the UNFCCC (the Climate Convention). On 21 December 1993, the Climate Conven-tion was ratified by a sufficient number of countries, including Denmark, for it to enter into force on 21 March 1994. One of the provisions of the treaty was to stabilise the greenhouse gas emissions from the industrial-ised nations by the end of 2000. At the first conference under the UN
22
Climate Convention in March 1995, it was decided that the stabilisation goal was inadequate. At the third conference in December 1997 in Kyoto in Japan, a legally binding agreement was reached committing the indus-trialised countries to reduce the six greenhouse gases by 5.2% by 2008-2012 compared with 1990 levels. However, for the F-gases, the nations can choose freely between 1990 and 1995 as the base year. On May 16, 2002, the Danish parliament voted for the Danish ratification of the Kyoto Protocol. Denmark is, thus, under a legal commitment to meet the requirements of the Kyoto Protocol, when it came into force on 16 Febru-ary 2005. The European Union must reduce emissions of greenhouse gases by 8%. However, within the EU, Member States have made a po-litical agreement – the Burden Sharing Agreement – on the contributions to be made by each state to the overall EU reduction level of 8%.
Under the Burden Sharing Agreement, Denmark must reduce emissions by an average of 21% in the period 2008-2012 compared with the 1990 emission level.
In accordance with the Kyoto Protocol, Denmark’s base year emissions include the emissions of CO2, CH4 and N2O in 1990 in CO2-equivalents and the emissions of HFCs, PFCs and SF6 in 1995 in CO2-equivalents. Furthermore, removal by sinks is included in the net emissions.
'����������������������-�����The European Union (EU) is a party to the UNFCCC and the Kyoto Pro-tocol. Therefore, the EU has to submit similar datasets and reports for the collective 15 EU Member States. The EU imposes some additional guide-lines to EU Member States through the EU Greenhouse Gas Monitoring Mechanism, to guarantee that the EU meets its reporting commitments.
NERI, Aarhus University, is responsible for the annual preparation and submission to the UNFCCC and the EU of the National Inventory Report and the GHG inventories in the Common Reporting Format in accor-dance with the UNFCCC Guidelines. NERI participates in meetings in the Conference of Parties (COP) to the UNFCCC and its subsidiary bod-ies, where the reporting rules are negotiated and settled. Furthermore, NERI participates in the EU Monitoring Mechanism on greenhouse gases, where the guidelines and methodologies on inventories to be pre-pared by the EU Member States are regulated.
The work concerning the annual greenhouse emission inventory is car-ried out in co-operation with other Danish ministries, research institutes, organisations and companies:
Danish Energy Authority, The Ministry of Economic and Business Af-fairs: Annual energy statistics in a format suitable for the emission inventory work and fuel-use data for the large combustion plants.
Danish Environmental Protection Agency, The Ministry of the Environ-ment. Database on waste and emissions of the F-gases.
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Statistics Denmark, The Ministry of Economic and Business Affairs. Sta-tistical yearbook, sales statistics for manufacturing industries and agri-cultural statistics.
Faculty of Agricultural Sciences, Aarhus University. Data on use of mi-neral fertiliser, feeding stuff consumption and nitrogen turnover in ani-mals.
The Road Directorate, The Ministry of Transport and Energy. Number of vehicles grouped in categories corresponding to the EU classification, mileage (urban, rural, highway), trip speed (urban, rural, highway).
Danish Centre for Forest, Landscape and Planning, University of Copen-hagen. Background data for Forestry and CO2 uptake by forest.
Civil Aviation Agency of Denmark, The Ministry of Transport and En-ergy. City-pair flight data (aircraft type and origin and destination air-ports) for all flights leaving major Danish airports.
Danish Railways, The Ministry of Transport and Energy. Fuel-related emission factors for diesel locomotives.
Danish companies. Audited green accounts and direct information gath-ered from producers and agency enterprises.
Formerly, the provision of data was on a voluntary basis, but more for-mal agreements are now prepared.
The background data (activity data and emission factors) for estimation of the Danish emission inventories is collected and stored in central da-tabases located at NERI. The databases are in Access format and handled with software developed by the European Environmental Agency and NERI. As input to the databases, various sub-models are used to esti-mate and aggregate the background data in order to fit the format and level in the central databases. The methodologies and data sources used for the different sectors are described in Chapter 1.4 and Chapters 3 to 9. As part of the QA/QC plan (Chapter 1.6), the data structure for data processing support the pathway from collection of raw data to data compilation, modelling and final reporting.
For each submission, databases and additional tools and submodels are frozen together with the resulting CRF-reporting format. This material is placed on central institutional servers, which are subject to routine back-up services. Material which has been backed up is archived safely. A fur-ther documentation and archiving system is the official journal for NERI. In this journal system, correspondence, both in-going and out-going, is registered, which in this case involves the registration of submissions and communication on inventories with the UNFCCC Secretariat, the European Commission, review teams, etc.
24
Figure 1.1 shows a schematic overview of the process of inventory preparation. The figure illustrates the process of inventory preparation from the first step of collecting external data to the last step, where the reporting schemes are generated for the UNFCCC and EU (in the CRF format (Common Reporting Format)) and to the United Nations Eco-nomic Commission for Europe/Cooperative Programme for Monitoring and Evaluation of the Long-range Transmission of Air Pollutants in Europe (UNECE/EMEP) (in the NFR format (Nomenclature For Report-ing)). For data handling, the software tool is CollectER (Pulles et al., 1999) and for reporting the software tool is the new CRF reporter tool developed by the UNFCCC Secretariat together with additional tools developed by NERI. Data files and programme files used in the inven-tory preparation process are listed in Table 1.1.
�� ��� List of current data structure; data files and programme files in use
Denmark’s air emission inventories are based on the Revised 1996 Inter-governmental Panel on Climate Change (IPCC) Guidelines for National Greenhouse Gas Inventories (IPCC, 1997), the Good Practice Guidance and Uncertainty Management in National Greenhouse Gas Inventories (IPCC, 2000) and the CORINAIR methodology. CORINAIR (COoRdina-tion of INformation on AIR emissions) is a European air emission inven-tory programme for national sector-wise emission estimations, harmo-nised with the IPCC guidelines. To ensure estimates are as timely, con-sistent, transparent, accurate and comparable as possible, the inventory programme has developed calculation methodologies for most subsec-tors and software for storage and further data processing (EMEP/CORINAIR, 2004).
A thorough description of the CORINAIR inventory programme used for Danish emission estimations is given in Illerup et al. (2000). The CORINAIR calculation principle is to calculate the emissions as activities multiplied by emission factors. Activities are numbers referring to a spe-cific process generating emissions, while an emission factor is the mass of emissions per unit activity. Information on activities to carry out the CORINAIR inventory is largely based on official statistics. The most con-sistent emission factors have been used, either as national values or de-fault factors proposed by international guidelines.
External data Sub-
modelsCentral
database
International
guidelines
Calculation of emission estimates
Report for all sources and pollutants
Final reports
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26
A list of all subsectors at the most detailed level is given in Illerup et al., 2000 together with a translation between CORINAIR and IPCC codes for sector classifications.
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Stationary combustion plants are part of the CRF emission sources ������������� ���� , ��������������������� ���� and ���������� ����� .
The Danish emission inventory for stationary combustion plants is based on the CORINAIR system described in the Emission Inventory Guide-book (EMEP/CORINAIR, 2004). The inventory is based on activity rates from the Danish energy statistics and on emission factors for different fuels, plants and sectors.
The Danish Energy Authority aggregates fuel consumption rates in the official Danish energy statistics to SNAP categories.
For each of the fuel and SNAP categories (sector and e.g. type of plant), a set of general emission factors has been determined. Some emission fac-tors refer to the EMEP/CORINAIR guidebook and some are country-specific and refer to Danish legislation, Danish research reports or calcu-lations based on emission data from a considerable number of plants.
Some of the large plants, such as e.g. power plants and municipal waste incineration plants are registered individually as large point sources and emission data from the actual plants are used. This enables use of plant specific emission factors that refer to emission measurements stated in annual environmental reports, etc. At present, the emission factors for CO2, CH4 and N2O are, however, not plant-specific, whereas emission factors for SO2 and NOX often are.
The CO2 from incineration of the plastic part of municipal waste is in-cluded in the Danish inventory.
In addition to the detailed emission calculation in the national approach, CO2 emission from fuel combustion is aggregated using the reference approach. In 2005, the CO2 emission inventory based on the reference approach and the national approach, respectively, differ by -1.15 %.
Please refer to Chapter 3 and Annex 3A for further information on emis-sion inventories for stationary combustion plants.
The specific methodologies regarding Fugitive Emissions from Fuels
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Off-shore activities: Emissions from offshore activities are estimated using the methodology described in the Emission Inventory Guidebook (EMEP/CORINAIR, 2004). The sources include emissions from the extraction of oil and gas, on-shore oil tanks, and onshore and offshore loading of ships. The emis-sion factors are based on the figures given in the guidebook, except for the onshore oil tanks where national values are used.
27
Oil Refineries – Petroleum products processing: The VOC emissions from petroleum refinery processes cover non-combustion emissions from feedstock handling/storage, petroleum products processing, product storage/handling and flaring. SO2 is also emitted from the non-combustion processes and includes emissions from processing the products and from sulphur recovery plants. The emission calculations are based on information from the Danish refineries and the energy statistics.
Please refer to Chapter 3 for further information on fugitive emissions from fuels.
Natural gas transmission and distribution: Inventories of the CH4 emission from gas transmission and distribution is based on annual environmental reports from the Danish gas transmis-sion company, Gastra (former DONG) and on a Danish inventory for the years 1999-2005, reported by the Danish gas sector (transmission and dis-tribution companies).
*�/��� ���������
The emissions from transport, referring to SNAP category 07 (road transport) and the sub-categories in 08 (other mobile sources), are made up in the IPCC categories: 1A3b (road transport), 1A2f (Industry-other), 1A3a (Civil aviation), 1A3c (Railways), 1A3d (Navigation), 1A4c (Agri-culture/forestry/fisheries), 1A4b (Residential) and 1A5 (Other).
An internal NERI model with a structure similar to the European COPERT III emission model (Ntziachristos, 2000) is used to calculate the Danish annual emissions for road traffic. For most vehicle categories, updated fuel use and emission data from the new COPERT IV version is incorporated in the NERI model. The emissions are calculated for opera-tionally hot engines, during cold start and fuel evaporation. The model also includes the emission effect of catalyst wear. Input data for vehicle stock and mileage is obtained from the Danish Road Directorate, and is grouped according to average fuel consumption and emission behav-iour. For each group the emissions are estimated by combining vehicle and annual mileage numbers with hot emission factors, cold:hot ratios and evaporation factors (Tier 2 approach).
For air traffic, the 2001-2005 estimates are made on a city-pair level, us-ing flight data from the Danish Civil Aviation Agency (CAA-DK) and LTO and distance-related emission factors from the CORINAIR guide-lines (Tier 2 approach). For previous years the background data consists of LTO/aircraft type statistics from Copenhagen Airport and total LTO numbers from CAA-DK. With appropriate assumptions, consistent time-series of emissions are produced back to 1990, which also include the findings from a Danish city-pair emission inventory in 1998.
Off-road working machines and equipment are grouped in the following sectors: inland waterways, agriculture, forestry, industry, and household and gardening. In general, the emissions are calculated by combining in-formation on the number of different machine types and their respective
28
load factors, engine sizes, annual working hours and emission factors (Tier 2 approach).
For the most important ferry routes in Denmark (a sub-part of Naviga-tion (1A3d)) detailed calculations are made by combining annual num-ber of return trips, sailing time, engine size, load factor and emission fac-tors (Tier 2 approach).
The most thorough recalculations have changed the estimates for road transport, sea transport and fisheries. The recalculations influence the CH4 emission factors and the emission estimates of CO2, CH4 and N2O for the sectors Agriculture/forestry/fisheries (1A4c) and Navigation (1A3d).
For transport, the CO2 emissions are determined with the lowest uncer-tainty, while the levels of the CH4 and N2O estimates are significantly more uncertain. The overall uncertainties in 2005 for CO2, CH4 and N2O are around 5, 35 and 65%, while the 1990-2005 emission trend uncertain-ties for the same three components are 5, 7 and 255%, respectively.
Please refer to Chapter 3 and Annex 3B for further information on emis-sions from transport.
*�/��� �#!����� �;���������
Energy consumption associated with industrial processes and the emis-sions thereof are included in the Energy sector of the inventory. This is due to the overall use of energy balance statistics for the inventory.
�����%�������7�+767�There is only one producer of cement in Denmark, Aalborg Portland Ltd. The activity data for the production of cement and the emission factor are obtained from the company as accounted for and published in the "Green National Accounts" (In Danish: “Grønne regnskaber”) worked out by the company according to obligations under Danish law. These accounts are subject to audit. The emission factor is produced as a result of a weighting of the emission factors from the production of low alkali cement, rapid cement, basis cement and white cement.
�"�������%�������7�+787�The reference for the activity data for production of lime, hydrated lime, expanded clay products and bricks are the production statistics from the manufacturing industries, published by Statistics Denmark. The produc-tion of lime and yellow bricks gives rise to CO2 emissions. The emission factors are based on stoichiometric relations, assumption on CaCO3 con-tent in clay as well as a default emission factor for expanded clay prod-ucts.
(���"�2���������"�������%�������7�+7;7�Limestone is used for the refining of sugar as well as for wet flue gas cleaning at power plants and waste incineration plants. The reference for the activity data is Statistics Denmark for sugar, Energinet.dk for gyp-
29
sum from power plants and National Waste Statistics for gypsum from waste incineration. The emission factors are based on stoichiometric rela-tions between consumption of CaCO3 and gypsum generation as well as consumption of lime for sugar refining and precipitation with CO2.
�"�������%�������7�+7<7�The reference for the activity data is Statistics Denmark for consumption of roofing materials, combined with technical specifications for roofing materials produced in Denmark. The emission factors are default factors.
(���"�2��������"�������%�������7�+7=7�The reference for the activity data is Statistics Denmark for consumption of asphalt and cut-back asphalt. The emission factors are default factors for consumption of asphalt and an estimated emission factor for cut-back asphalt based on the statistics on the emission of NMVOC compiled by the industrial organisations in question.
2��������"�������%�������7�+7>7�The reference for activity data for the production of glass and glass wool are obtained from the producers published in their environmental re-ports. Emission factors are based on stoichiometric relations between raw materials and CO2 emissions.
2��������"�������%�������7��787�There is one producer. To date, the data in the inventory relies on infor-mation from the producer. The producer reports emissions of NOx and NH3 as measured emissions and emissions of N2O for 2003 as estimated emissions. The emission of N2O in 2005 is zero as the nitric acid produc-tion was closed down in the middle of 2004.
2��������"�������%�������7��7<������7��There is one producer. The data in the inventory relies on information published by the producer in environmental reports.
"�������%�������7�.767�There is one producer. The activity data as well as data on consumption of raw materials (coke) has been published by the producer in environ-mental reports. Emission factors are based on stoichiometric relations be-tween raw materials and CO2 emission.
,��849���"�849���"� �����������(���"�2��������"�������%��������'�,����8497&��The inventory on the F-gases (HFCs, PFCs and SF6) is based on work car-ried out by the Danish Consultant Company "Planmiljø". Their yearly report (Danish Environmental Protection Agency, 2007)� is available in English as documentation of inventory data up to the year 2005. The methodology is implemented for the whole time-series 1990-2005, but full information on activities only exists since 1995 (1993).
30
Please refer to Chapter 4 and Annex 3.C for further information on in-dustrial processes.
*�/�/� � �����
.5&�'�,���;7+�27� ��������,���(���"�"����������1�������"��������"��������The approach for calculating the emissions of Non-Methane Volatile Or-ganic Carbon (NMVOC) from industrial and household use in Denmark focuses on single chemicals rather than activities. This leads to a clearer picture of the influence from each specific chemical, which enables a more detailed differentiation on products and the influence of product use on emissions. The procedure is to quantify the use of the chemicals and estimate the fraction of the chemicals that is emitted as a conse-quence of use.
Simple mass balances for calculating the use and emissions of chemicals are set up 1) use = production + import – export, 2) emission = use * emission factor. Production, import and export figures are extracted from Statistics Denmark, from which a list of 427 single chemicals, a few groups and products is generated. For each of these, a “use” amount in tonnes per year (from 1995 to 2005) is calculated. It is found that 44 dif-ferent NMVOCs comprise over 95% of the total use and it is these 44 chemicals that are investigated further. The “use” amounts are distrib-uted across industrial activities according to the Nordic SPIN (Sub-stances in Preparations in Nordic Countries) database, where informa-tion on industrial use categories and products is available in a NACE coding system. The chemicals are also related to specific products. Emis-sion factors are obtained from regulators or the industry.
Outputs from the inventory are: a list where the 44 most predominant NMVOCs are ranked according to emissions to air; specification of emis-sions from industrial sectors and from households - contribution from each chemical to emissions from industrial sectors and households; tidal (annual) trend in NMVOC emissions, expressed as total NMVOC and single chemical, and specified in industrial sectors and households.
Please refer to Chapter 5 for further information on emission inventories for solvents.
*�/�,� 7(���! �!���
.5&�'�,���@7+�&7� ��������,���(���"�"��������(��������The emission is given in CRF: Table 4 Sectoral Report for Agriculture and Table 4.A, 4.B(a), 4.B(b) and 4.D Sectoral Background Data for Agri-culture. The calculation of emissions from the agricultural sector is based on methods described in the IPCC Guidelines (IPCC, 1996) and the Good Practice Guidance (IPCC, 2000). Activity data for livestock is on a one-year average basis from the agriculture statistics published by Statistics Denmark (2005). Data concerning the land use and crop yield is also from the agricultural statistics. Data concerning the feed consumption and nitrogen excretion is based on information from the Faculty of Agri-cultural Science, University of Aarhus. The CH4 Implied Emission Fac-tors for Enteric Fermentation and Manure Management are based on a Tier 2 approach for all animal categories. All livestock categories in the Danish emission inventory are based on an average of certain subgroups
31
separated by differences in animal breed, age and weight class. The emission from enteric fermentation for poultry and fur farming is not es-timated. There is no default value recommended in the IPCC guidelines (Table A-4 in Good Practice Guidance).
Emission of N2O is closely related to the nitrogen balance. Thus, quite a lot of the activity data is related to the Danish calculations for ammonia emission (Hutchings et al., 2001, Mikkelsen et al., 2005). National stan-dards are used to estimate the amount of ammonia emission. When es-timating the N2O emission the IPCC standard value is used for all emis-sion sources. The emission of CO2 from Agricultural Soils is included in the LULUCF sector.
A model-based system is applied for the calculation of the emissions in Denmark. This model (DIEMA – Danish Integrated Emission Model for Agriculture) is used to estimate emission from both greenhouse gases and ammonia. A more detailed description is published in Mikkelsen et al. (2005). The emission from the agricultural sector is mainly related to livestock production. DIEMA works on a detailed level and includes around 30 livestock categories, and each category is subdivided accord-ing to stable type and manure type. The emission is calculated from each subcategory and the emission is aggregated in accordance with the live-stock category given in the CRF.
To ensure data quality, both data used as activity data and background data used to estimate the emission factor are collected, and discussed in cooporation with specialists and researchers in different institutes and research sections. Thus, the emission inventory will be evaluated con-tinuously according to the latest knowledge. Furthermore, time-series both of emission factors and emissions in relation to the CRF categories are prepared. Any considerable variations in the time-series are ex-plained.
The uncertainties for assessment of emissions from enteric fermentation, manure management and agricultural soils have been estimated based on a Tier 1 approach. The most significant uncertainties are related to the N2O emission.
A more detailed description of the methodology for the agricultural sec-tor is given in Chapter 6 and Annex 3D.
���������(���"�2����������"�-���.���(����"�&�����7�As in previous submissions for forest land remaining forest land, only carbon (C) stock change in living biomass is reported. Change in C stocks is based on Equation 3.2.1 in the IPCC GPG (IPCC, 2000), where C lost due to annual harvests is subtracted from C sequestered in growing biomass for the area of forest land remaining forest land. The data for forest area and growth rates are obtained from the latest Forestry Census conducted in 2000 and remain similar during the period 2000-2005. The data for the amount of wood annually harvested are obtained from Sta-tistics Denmark. Wood volumes are converted to C stocks by a combina-tion of country-specific values, literature values from the northwest
32
European region and default values. There were no changes in method-ology for the 2007 submission.
For cropland converted to forest land (afforestation), the reported change in C stock also concerned living biomass only. The change in C stock is estimated using a model based on country-specific increment tables for oak (representing broadleaves) and Norway spruce (representing coni-fers). The model calculates annual growth for annual cohorts of affore-station areas since 1990. Data on annual afforestation area is for the most part obtained from the Danish Forest and Nature Agency (subsidised private afforestation, municipal afforestation and afforestation by state forest districts). Afforestation by private landowners without subsidies was based on total afforested area recorded by the Forestry Census 2000 for the period 1990-99, with subtraction of the above categories of affore-station. Wood volumes estimated by the model are converted to C-stocks as for forest land remaining forest land. There is as yet no harvesting conducted in the young afforested stands. No changes in methodology or recalculations were done for the 2006 submission.
CO2 emissions from cropland and grassland are based on census data from Statistics Denmark as regards size of area and crop yield combined with GIS-analysis on land use. The emission from mineral soils for both cropland and grassland is estimated with a three-pooled dynamical soil C model (C-TOOL). C-TOOL was initialised in 1980. The model is run for each county in Denmark. Emissions from organic soils are based on IPCC Tier 1b. The area with organic soils is based on soil maps combined with field-specific crop data. National models have been developed for the horticultural area based on area statistics from Statistic Denmark. Sinks in hedgerows are based on a national developed model. The area with hedgerows is based on hedgerows established with financial sup-port from the Danish Government. Emissions from liming are based on annual sales data collected by the Danish Agricultural Advisory Centre, combined with the acid neutralisation capacity for each lot produced. The acid neutralisation capacity is estimated by the Danish Plant Direc-torate. “Settlement” and “Other land” is not estimated.
$����7�For 6.A Solid Waste Disposal on Land, only managed waste disposal is of importance and registered. The data used for the amounts of munici-pal solid waste deposited at solid waste disposal sites is according to the official registration performed by the Danish Environmental Protection Agency (DEPA). The data is registered in the ISAG database, where the latest yearly report is Danish Environmental Protection Agency (2006). CH4 emissions from solid waste disposal sites are calculated with a model suited to Danish conditions. The model is based on the IPCC Tier 2 approach using a First Order Decay approach. The model is unchanged for the whole time-series. The model is described in Chapter 8.
For 6.B Waste Water Handling, country-specific methodologies for calcu-lating the emissions of CH4 and N2O at wastewater treatment plants (WWTPs) were prepared and implemented for the 2005 submissions. Some adjustments to data in this methodology was made for the 2006
33
submission, for this submission the methodology was basically un-changed.
The methodology for ��� is developed following the IPCC Guidelines and the IPCC Good Practice Guidance. The data available for the volume of wastewater is registered by DEPA. The wastewater flow to WWTPs and the resulting sludge consists of a municipal and industrial part. From the registration performed by DEPA, no data exists to allow for a separation of the domestic/municipal contribution from the industrial contribution. A significant fraction of the industrial wastewater is treated at centralised municipal WWTPs. In addition, it is not possible to sepa-rate the contribution to methane emission from sludge versus wastewa-ter. The methodology is based on information on the amount of organic degradable matter in the influent wastewater and the fraction which is treated by anaerobic wastewater treatment processes. The amount of CH4 not emitted, the CH4 recovered or combusted, has been calculated based on yearly reported national final sludge disposal data from DEPA. No emissions originating from on-site industrial treatment processes have been included.
For the methodology for ��� emissions, both anaerobic and aerobic conditions have been considered. The methodology has been divided into two parts, i.e. direct and indirect emissions. The direct emission originates from wastewater treatment processes at the WWTPs and a minor indirect emission contribution originates from the effluent’s con-tent of nitrogen compounds. The direct emission from wastewater treat-ment processes is calculated according to the equation:
������������������� � ���������� ����� ,,,, 22⋅⋅=
where �����is the size of the Danish population, ���������� is the fraction of the Danish population connected to the municipal sewer system (90%) and ������ �������� is the emission factors. The latter has been adjusted by a correction factor, accounting for an increasing influent of nitrogen-containing wastewater from industry from 1990 to 1998, after which the industrial contribution reached a constant level. The methodology for calculation of the indirect N2O emission includes emissions from human sewage based on annual per capita protein intake, improved by includ-ing the fraction of non-consumption protein in domestic wastewater. Emission of N2O originating from effluent-recipient nitrogen discharges from the following point sources has been included: industry discharges, rainwater conditioned effluents, effluent from scattered houses, effluent from mariculture and fish farming and effluent from municipal and pri-vate WWTPs. Data on nitrogen effluent contributions has been obtained from national statistics.
������ ��������������� All waste incinerated is used for energy and heat production. This production is included in the energy statistics, hence emissions are included in � ��!�"#��������$�"#����#������������������$��%�������. Only very small emissions due to gasification of waste are in-cluded here.
Please refer to Chapter 8 and Annex 3E for further information on emis-sion inventories for waste.
34
*�,� <���.�#�����������.�������!��������(������
A key source analysis for year 2005 has been carried out in accordance with the IPCC Good Practice Guidance. The analyses, as regards the ba-sic source categorisation, have been kept unchanged since the analyses for the submissions in 2002, 2003, 2004, 2005 and 2006. The source cate-gorisation used results in a total of 72 sources, of which 16 are identified as key sources due to both level and trend key source analysis. The en-ergy sector and CO2 emissions from stationary combustion contribute to those 16 key sources with 6 key sources, of which CO2 from coal contrib-utes most with 22.8% of the national total. The category, CO2 emissions from mobile combustion and road transportation, is also a key source and the second highest contributor, with 19.0%. CO2 from natural gas is the third largest contributor with 16.9%. In the agricultural sector, there are 4 trend and level key sources, of which 3 are among the 7 highest contributors to the national total. These three sources are direct N2O emissions from agriculture soils, indirect N2O emissions from nitrogen used in agriculture and CH4 from enteric fermentation, contributing 4.7, 4.2 and 4.1%, respectively, to the national total in 2005. The fourth agri-cultural key source is CH4 from manure management contributing 1.6%. N2O from manure management is a key source to level only and con-tributes 0.9%. Finally, the industrial sector contributes with 2 level and trend key sources: CO2 from cement production (contributes 2.3%) and emissions from substitutes for ODS, F-gases (1.3%). The waste sector in-cludes one level key source, which is CH4 from solid waste disposal on land, contributing 1.6% to the national total. The categorisation used, re-sults, etc. are included in Annex 1. All comparisons were made to na-tional total in CO2-equivalents without LULUCF.
This section outlines the Quality Control (QC) and Quality Assurance (QA) plan for greenhouse gas emission inventories performed by the Danish National Environmental Research Institute (Sørensen et al., 2005). The plan is in accordance with the guidelines provided by the UNFCCC (IPCC, 1997), and the Good Practice Guidance and Uncertainty Man-agement in National Greenhouse Gas Inventories (IPCC, 2000). The ISO 9000 standards are also used as important input for the plan.
*����� '��������.�>!� ����&����
The quality planning is based on the following definitions as outlined by the ISO 9000 standards as well as the Good Practice Guidance (IPCC, 2000):
• Quality management (&�) Coordinates activity to direct and control with regard to quality.
• Quality Planning (&$) Defines quality objectives including specifica-tion of necessary operational processes and resources to fulfil the quality objectives.
• Quality Control (&�) Fulfils quality requirements.
35
• Quality Assurance (&�) Provides confidence that quality require-ments will be fulfilled.
• Quality Improvement (&) Increases the ability to fulfil quality re-quirements.
The activities are considered inter-related in this report as shown in Fig-ure 1.2.
�������� Interrelation between the activities with regard to quality. The arrows are ex-plained in the text below this figure.
1: The &$ sets up the objectives and, from these, measurable properties valid for the &�.
2: The &� investigates the measurable properties that are communicated to &� for assessment in order to ensure sufficient quality.
3. The &$ identifies and defines measurable indicators for the fulfilment of the quality objectives. This yields the basis for the &� and has to be supported by the input coming from the &�.
4: The result from &� highlights the degree of fulfilment for every qual-ity objective. It is thus a good basis for suggestions for improvements to the inventory to meet the quality objectives.
5: Suggested improvements in the quality may induce changes in the quality objectives and their measurability.
6: The evaluation carried out by external authorities is important input when improvements in quality are being considered.�
*����� ��.�������.�>!� ����
A solid definition of quality is essential. Without such a solid definition, the fulfilment of the objectives will never be clear and the process of quality control and assurance can easily turn out to be a fuzzy and un-pleasant experience for the people involved. On the contrary, in case of a solid definition and thus a clear goal, it will be possible the make a valid statement of “good quality” and thus form constructive conditions and motivate the inventory work positively. A clear definition of quality has not been given in the UNFCCCC guidelines. In the Good Practice Guid-ance, Chapter 8.2, however, it is mentioned that:
“Quality control requirements, improved accuracy and reduced uncer-tainty need to be balanced against requirements for timeliness and cost
Quality assurance (A+) Quality control (A.)
Quality improvement (QI)
Quality planning (QP)
1
2
3
5 4 6
36
effectiveness.” The statement of balancing requirements and costs is not a solid basis for QC as long as this balancing is not well defined.
The resulting standard of the inventory is defined as being composed of accuracy and regulatory usefulness. The goal is to maximise the standard of the inventory and the following statement defines the quality objec-tive:
A Critical Control Point (��$) is defined in this submission as an ele-ment or an action which needs to be taken into account in order to fulfil the quality objectives. Every ��$ has to be necessary for the objectives and the ��$ list needs to be extended if other factors, not defined by the ��$ list, are needed in order to reach at least one of the quality objec-tives.
The objectives for the &�, as formulated by IPCC (2000), are to improve elements of transparency, consistency, comparability, completeness and confidence. In the UNFCCC guidelines (IPCC, 1997), the element “confi-dence” is replaced by “accuracy” and in this plan “accuracy” is used.
The objectives for the &� are used as ��$s, including the elements men-tioned above. The following explanation is given by UNFCCC guidelines (IPCC, 1997) for each ��$:�
!��� *����� means that the assumptions and methodologies used for an inventory should be clearly explained to facilitate replication and as-sessment of the inventory by users of the reported information. The transparency of the inventories is fundamental to the success of the process for communication and consideration.
��� � ���� means that an inventory should be internally consistent in all its elements with inventories of other years. An inventory is consistent if the same methodologies are used for the base and for all subsequent years and if consistent datasets are used to estimate emissions or remov-als from source or sinks. Under certain circumstances, an inventory us-ing different methodologies for different years can be considered to be consistent if it has been recalculated in a transparent manner in accor-dance with the Intergovernmental Panel on Climate Change (IPCC) guidelines and good practice guidance.
��+*���"�#�� means that estimates of emission and removals reported by Annex I Parties in inventories should be comparable among Annex I par-ties. For this purpose, Annex I Parties should use the methodologies and formats agreed upon by the COP for estimating and reporting invento-ries. The allocation of different source/sink categories should follow the split of �)� ����//��$���0����#��� ������������#�0������� ��0� ��)������� �(IPCC, 1997) at the level of its summary and sectoral tables.
��+*#����� �means that an inventory covers all sources and sinks, as well as all gases, included in the IPCC guidelines as well as other exist-
37
ing relevant source/sink categories, which are specific to individual An-nex I Parties and, therefore, may not be included in the IPCC guidelines. Completeness also means full geographic coverage of sources and sinks of an Annex I Party.
������� is a relative measure of the exactness of an emission or removal estimate. Estimates should be accurate and the sense that they are sys-tematically neither over nor under true emissions or removals, as far as can be judged, and that uncertainties are reduced as far as practicable. Appropriate methodologies should be used in accordance with the $��������*����������������, to promote data accuracy in inventories.
The robustness against unexpected disturbance of the inventory work has to be high in order to secure high quality, which is not covered by the ��$s above. The correctness of the inventory is formulated as an in-dependent objective. This is so because the correctness of the inventory is a condition for all other objectives to be effective. A large part of the Tier 1 procedure given by the Good Practice Guidance (IPCC, 2000) is actu-ally checks for miscalculations and, thus, supports the objective of cor-rectness. Correctness, as defined here, is not similar to accuracy, because the correctness takes into account miscalculations, while accuracy relates to minimising the always present data-value uncertainty.
�"� ��� implies arrangement of inventory work as regards e.g. inven-tory experts and data sources in order to minimise the consequences of any unexpected disturbance due to external and internal conditions. A change in an external condition could be interruption of access to an ex-ternal data source and an internal change could be a sudden reduction in qualified staff, where a skilled person suddenly leaves the inventory work.
��������� has to be secured in order to avoid uncontrollable occurrence of uncertainty directly due to errors in the calculations.
The different ��$s are not independent and represent different degrees of generality. E.g. deviation from ��+*���"�#�� may be accepted if a high degree of ���� *����� is applied. Furthermore, there may even be a con-flict between the different ��$s. E.g. new knowledge may suggest im-provements in calculation methods for better ��+*#����� , but the same improvements may to some degree violate the ��� � ���� and ��+*���"�#%�� criteria with regard to earlier years’ inventories and the reporting from other nations. It is, therefore, a multi-criteria problem of optimisa-tion to apply the set of ��$s in the aim for good quality.
*���,� ;�������������#�='�
The strategy is based on a process-oriented principle (ISO 9000 series) and the first step is, thus, to set up a system for the process of the inven-tory work. The product specification for the inventory is a dataset of emission figures and the process, thereby, equates with the data flow in the preparation of the inventory.
The data flow needs to support the QC/QA in order to facilitate a cost-effective procedure. The flow of data has to take place in a transparent way by making the transformation of data detectable. It should be easy
38
to find the original background data for any calculation and to trace the sequence of calculations from the raw data to the final emission result. Computer programming for automated calculations and checking will enhance the accuracy and minimise the number of miscalculations and flaws in input value settings. Especially manual typing of numbers needs to be minimised. This assumes, however, that the quality of the pro-gramming has been verified to ensure the correctness of the automated calculations. Automated value control is also one of the important means to secure accuracy. Realistic uncertainty estimates are necessary for se-curing accuracy, but they can be difficult to produce due to the uncer-tainty related to the uncertainty estimates themselves. It is, therefore, important to include the uncertainty calculation procedures into the data structure as far as possible. The QC/QA needs to be supported as far as possible by the data structure; otherwise the procedures can easily be-come troublesome and subject to frustration.
Both data processing and data storage form the data structure. The data processing is carried out using mathematical operations or models. The models may be complicated where they concern human activity or be simple summations of lower aggregated data. The data storage includes databases and file systems of data that are either calculated using the data processing at the lower level, using input to new processing steps or even using both output and input in the data structure. The measure for quality is basically different for processing and storage, so these need to be kept separate in a well-designed quality manual. A graphical display of the data flow is seen in Figure 1.3 and explained in the following.
The data storage takes place for the following types of data:
������������ a single numerical value of a parameter coming from an external source. These data govern the calculation of �+� ������#��#��������*����
��� �������������������� Data for input to the final emission calcula-tion in terms of data for release source strength and activity. The data is directly applicable for use in the standardised forms for calculation. These data are calculated using external data or represent a direct use of �.�����#�1��� when they are directly applicable for �+� ������#��#����� .
��� ������� Estimated emissions based on the �+� ������#��#��������%*����
��� ��� ���������� Reporting of emission data in requested formats and aggregation level.
39
�������� The general data structure for the emission inventory.
Key levels are defined in the data structure as:
������������� �������������������Collection of external data for calculation of emission factors and activity data. The activity data are collected from different sectors and statistical surveys, typically reported on a yearly basis. The data consist of raw data, having an identical format to the data received and gathered from external sources. Level 1 data acts as a base-set, on which all subsequent calculations are based. If alterations in calculation procedures are made, they are based on the same dataset. When new data are introduced they can be implemented in accordance with the QA/QC structure of the in-ventory.
������������� ������Data directly usable for the inventory This level represents data that have been prepared and compiled in a form that is directly applicable for calculation of emissions. The com-piled data are structured in a database for internal use as a link between more or less raw data and data that are ready for reporting. The data are compiled in a way that elucidates the different approaches in emission assessment: (1) directly on measured emission rates, especially for larger point sources, (2) based on activities and emission factors, where the value setting of these factors are stored at this level.
������������� ����, Emission data The emission calculations are reported by the most detailed figures and divided in sectors. The unit at this level is typically mass per year for the country. For sources included in the SNAP system, the SNAP level 3 is relevant. Internal reporting is performed at this level to feed the external communication of results.
External data
Emission calculation input
Emission Data
Emission Reporting
Calculating emission
Preparation of factors for emission
calculations
�������������� �����������
3��� �*
3��� ��
3��� ��
3��� �/
3��� �*
3��� ��
Calculating aggregated parameters 3��� ��
40
������������� ����)�Final reports for all subcategories The complete emission inventory is reported to UNFCCC at this level by summing up the results from every subcategory.
���������������� �����Compilation of external data�Preparation of input data for the emission inventory based on the exter-nal data sources. Some external data may be used directly as input to the data processing at level 2, while other data needs to be interpreted using more or less complicated models, which takes place at this level. The in-terpretation of activity data is to be seen in connection with availability of emission factors and vice versa. These models are compiled and proc-essed as an integrated part of the inventory preparation.
���������������� �����Calculation of inventory figures The emission for every subcategory is calculated, including the uncer-tainty for all sectors and activities. The summation of all contributions from sub-sources makes up the inventory.
���������������� �����Calculation aggregated parameters Some aggregated parameters need to be reported as part of the final re-porting. This does not involve complicated calculations but important figures, e.g. implied emission factors at a higher aggregated level to be compared in time-series and with other countries.
*����� ��.�������.�;�����.�A���!�������1��2�
The ��$s have to be based on clear measurable factors, otherwise the &$ will end up being just a loose declaration of intent. Thus, in the follow-ing, a series of $���� �������� ����� ($�) is identified as building blocks for a solid &�. Table 8.1 in Good Practice Guidance is a listing of such $�s. However, the listing in Table 1.1 below is an extended and modi-fied listing, in comparison to Table 8.1. in the Good Practice Guidance supporting all the ��$s. The $�s will be routinely checked in the QC reporting and, when external reviews take place, the reviewers will be asked to assess the fulfilment of the $�s using a checklist system. The list of $�s is continually evaluated and modified to offer the best possi-ble support for the ��$s. The actual list used is seen in Table 1.2.
41
�� ���� The list of ��s as used in Spring 2006.
Level CCP Id Description
Data Storage
level 1
1. Accuracy DS.1.1.1 General level of uncertainty for every dataset including the reasoning for the specific values
DS.1.1.2 Quantification of the uncertainty level of every single data value, including the reasoning for the specific values.
2. Comparability DS1.2.1 Comparability of the data values with similar data from other countries, which are comparable with Denmark, and evaluation of the discrepancy.
3.Completeness DS.1.3.1 Documentation showing that all possible national data sources are included, by setting down the reasoning behind the selection of datasets.
4.Consistency DS.1.4.1 The origin of external data has to be preserved whenever possible without explicit arguments (referring to other PMs)
6.Robustness DS.1.6.1 Explicit agreements between the external institution hold-ing the data and NERI about the conditions of delivery
DS.1.6.2 At least two employees must have a detailed insight into the gathering of every external dataset.
7.Transparency DS.1.7.1 Summary of each dataset including the reasoning behind the selection of the specific dataset
DS.1.7.2 The archiving of datasets needs to be easyily accessible for any person in the emission inventory
DS.1.7.3 References for citation for any external dataset have to be available for any single number in any dataset.
DS.1.7.4 Listing of external contacts for every dataset
Data Processing
level 1
1. Accuracy DP.1.1.1 Uncertainty assessment for every data source as input to Data Storage level 2 in relation to type of variability. (Dis-tribution as: normal, log normal or other type of variability)
DP.1.1.2 Uncertainty assessment for every data source as input to Data Storage level 2 in relation to scale of variability (size of variation intervals)
DP.1.1.3 Evaluation of the methodological approach using interna-tional guidelines
DP.1.1.4 Verification of calculation results using guideline values
2.Comparability DP.1.2.1 The inventory calculation has to follow the international guidelines suggested by UNFCCC and IPCC.
3.Completeness DP.1.3.1 Assessment of the most important quantitative knowledge which is lacking.
DP.1.3.2 Assessment of the most important cases where access is lacking with regard to critical data sources that could improve quantitative knowledge.
4.Consistency DP.1.4.1 In order to keep consistency at a high level, an explicit description of the activities needs to accompany any change in the calculation procedure
DP.1.4.2 Identification of parameters (e.g. activity data, constants) that are common to multiple source categories and con-firmation that there is consistency in the values used for these parameters in the emission calculations
5.Correctness DP.1.5.1 Shows at least once, by independent calculation, the correctness of every data manipulation
DP.1.5.2 Verification of calculation results using time-series
DP.1.5.3 Verification of calculation results using other measures
DP.1.5.4 Show one-to-one correctness between external data sources and the databases at Data Storage level 2
42
6.Robustness DP.1.6.1 Any calculation must be anchored to two responsible persons who can replace each other in the technical issue of performing the calculations.
7.Transparency DP.1.7.1 The calculation principle and equations used must be described
DP.1.7.2 The theoretical reasoning for all methods must be de-scribed
DP.1.7.3 Explicit listing of assumptions behind all methods
DP.1.7.4 Clear reference to dataset at Data Storage level 1
DP.1.7.5 A manual log to collect information about recalculations
Data Storage
level 2
2.Comparability DS.2.2.1 Comparison with other countries that are closely related to Denmark and explanation of the largest discrepancies
5.Correctness DS.2.5.1 Documentation of a correct connection between all data types at level 2 to data at level 1
DS.2.5.2 Check if a correct data import to level 2 has been made
6.Robustness DS.2.6.1 All persons in the inventory work must be able to handle and understand all data at level 2.
7.Transparency DS.2.7.1 The time trend for every single parameter must be graphi-cally available and easy to map
DS.2.7.2 A clear Id must be given in the dataset having reference to level 1.
Data Processing
level 2
1. Accuracy DP.2.1.1 Documentation of the methodological approach for the uncertainty analysis
DP.2.1.2 Quantification of uncertainty
2.Comparability DP.2.2.1 The inventory calculation has to follow the international guidelines suggested by UNFCCC and IPCC
6.Robustness DP.2.6.1 Any calculation at level 4 must be anchored to two re-sponsible persons who can replace each other in the technical issue of performing the calculations.
7.Transparency DP.2.7.1 Reporting of the calculation principle and equations used
DP.2.7.2 Reporting of the theoretical reasoning for all methods
DP.2.7.3 Reporting of assumptions behind all methods
DP.2.7.4 The reasoning for the choice of methodology for uncer-tainty analysis needs to be written explicitly.
Data Storage
level 3
1. Accuracy DS.3.1.1 Quantification of uncertainty
5.Correctness DS.3.5.1 Comparison with inventories of the previous years on the level of the categories of the CRF as well as on SNAP source categories. Any major changes are checked, verified, etc.
DS.3.5.2 Total emissions, when aggregated to CRF source catego-ries, are compared with totals based on SNAP source categories (control of data transfer).
DS.3.5.3 Checking of time-series of the CRF and SNAP source categories as they are found in the Corinair databases. Considerable trends and changes are checked and ex-plained.
7.Transparency DS.3.7.1 Documentation of a correct connection between all data types at DS3 to data at level DS2
Data Processing
level 3
7.Transparency DP.3.7.1 In the calculation sheets, there must be clear Id to Data Storage level 3 data
43
Data Storage
level 4
1. Accuracy DS.4.1.1 Questionnaire to external experts: The performance of the PMs that relate to accuracy.
2.Comparability DS.4.2.1 Description of similarities and differences in relation to other countries’ inventories for the methodological ap-proach.
3.Completeness DS.4.3.1 Questionnaire to external experts: The performance of the PMs that relate to completeness.
DS.4.3.2 National and international verification including explana-tion of the discrepancies.
4.Consistency DS.4.4.1 The inventory reporting must follow the international guidelines suggested by UNFCCC and IPCC.
7.Transparency DS.4.7.1 External review for evaluation of the communication per-formance.
*����� ; ��.������>!� ����&����
The IPCC uses the concept of a tiered approach, i.e. a stepwise approach, where complexity, advancement and comprehensiveness increase. Gen-erally, more detailed and advanced methods are recommended in order to give guidance to countries which have more detailed datasets and more capacity, as well as to countries with less available data and man-power. The tiered approach helps to focus attention on the areas of the inventories that are relatively weak, rather than investing effort in irrele-vant areas. Furthermore, the IPCC guidelines recommend using higher tier methods for key sources in particular. Therefore, the identification of key sources is crucial for planning quality work. However, there exist several issues regarding the listing of priority sources: (1) The contribu-tion to the total emission figure (key source listing); (2) The contribution to the total uncertainty; (3) Most critical sources in relation to implemen-tation of new methodologies and thus highest risk for miscalculations. All the points listed are necessary for different aspects of producing high quality work. These listings will be used to secure implementation of the full quality scheme for the most relevant sources. Verification in relation to other countries has been undertaken for priority sources.
*���4� ��� ����������.����=7:='�� ��
The PMs listed in Table 1.2 are described for each sector in the QA/QC sections of Chapters 3-8, where a status with regard to implementation is also given. Some of the PMs are the same for all sectors and a common description for these PMs is given in Section 1.6.10, below. The focus has been on level 1 for both data storage and data processing as this is the most labour-intensive part. The quality system will be evaluated and ad-justed continuously.
*���+� 7�����(��.�#�����#�#��!���������
The QA/QC work is supported by an inventory file system, where all data, models and QA/QC procedures and checks are stored as files in folders (Figure 1.4).
44
�������� Schematic diagram of the folder structure in the inventory file system.�
The inventory file system consists of the following levels: year, sector and the level for the process of the inventory work, as illustrated in Fig-ure 1.4. The first level in the file system is year, which here means the in-ventory year and not the calendar year. The sector level contains the PMs relevant for the individual sectors i.e. the first levels (DS1 and DP1) (ex-cept the PMs described in Section 1.6.10), while the rest of the PMs (DS2-4 and DP2-3), are common for all sectors.
All data, models and other QA/QC related files are stored in the inven-tory file system and are accesseble for all staff involved in the inventory work.
*���*��'�����=7:='�;A���
The following PMs are common for all the sectors:
����������(��3��� �*�
For the energy sector, two persons have detailed insight in data gather-ing, while this is only partly achieved for the other sectors. The plan is to fulfil this PM in 2007 and 2008.
Data Storage level 1
6. Robustness DS.1.6.2 At least two employees must have a detailed insight into the gathering of every external dataset.
45
All data, models and other QA/QC related files are stored in the inven-tory file system and are accessible for all inventory staff members. Refer to Section 1.6.9.
�������������(�3��� �*�
This PM is supported by the inventory file system where it is possible to compare and harmonise parameters that are common to multiple source categories.
All data, models and other QA/QC related files are stored in the inven-tory file system and are accessible for all inventory staff members. Refer to Section 1.6.9.
����������(��3��� ���
Systematic inter-country comparison has only been made on data stor-age level 4. Refer to DS 4.3.2.
This PM is fulfilled for all sectors except agriculture and land use change and forestry. The PM is supported by the inventory file system. Refer to Section 1.6.9.
Programs exist to make time-series for all parameters. A tool for graphi-cally showing time-series has not yet been developed.
An overview of all external data is given in DS 1.4.1 including ID num-bers for all external datasets. Many references already exist in the data-bases (level 2) which point to the original source of data, but ID numbers have to be implemented and extended to all data in the databases.
Data Storage level 1
7. Transparency DS.1.7.2 The archiving of datasets needs to be easy accessible for any person involved in the emission inventory.
Data Process-ing level 1
4. Consistency DP.1.4.2 Identification of parameters (e.g. activity data, constants) that are common to multiple source categories and confirmation that there is consistency in the values used for these parameters in the emission calculations.
Data Process-ing level 1
6.Robustness DP.1.6.1 Any calculation must be anchored to two responsible persons who can replace each other in the technical issue of performing the calculations.
Data Storage
level 2
2.Comparability DS.2.2.1 Comparison with other countries that are closely related to Denmark and explanation of the largest discrepancies.
Data Storage
level 2
6.Robustness DS.2.6.1 All persons in the inventory work must be able to handle and understand all data at level 2.
Data Storage
level 2
7.Transparency DS.2.7.1 The time trend for every single parameter must be graphically available and easy to map.
Data Storage
level 2
7.Transparency DS.2.7.2 A clear Id must be given in the dataset hav-ing reference to level 1.
46
�����;�������(�3��� ���
Refer to Section 1.7 in the Danish NIR.
Refer to Section 1.7 in the Danish NIR and the QA/QC sections in the sector chapters.
The emission calculations follow the international guidelines.
At present the emission calculations are carried out using applications developed at NERI. The software development and programme runs are anchored to two inventory staff members.
Due to the uniform treatment of input data in the calculation routines used by the NERI software programmes, a central documentation of cal-culation principles, equations, theoretical reasoning and assumptions must be given, treating all national emission sources. This documenta-tion still remains to be made, but is planned to be carried out in the fu-ture.
Due to the uniform treatment of input data in the calculation routines used by the NERI software programmes, a central documentation of cal-culation principles, equations, theoretical reasoning and assumptions must be given, treating all national emission sources. This documenta-tion still remains to be made, but is planned to be carried out in the fu-ture.
Due to the uniform treatment of input data in the calculation routines used by the NERI software programmes, a central documentation of cal-culation principles, equations, theoretical reasoning and assumptions
Data Processing
level 2
1. Accuracy DP.2.1.1 Documentation of the methodological ap-proach for the uncertainty analysis
Data Processing
level 2
1. Accuracy DP.2.1.2 Quantification of uncertainty
Data Processing
level 2
2.Comparability DP.2.2.1 The inventory calculation has to follow the international guidelines suggested by UNFCCC and IPCC.
Data Processing
level 2
6.Robustness DS.2.6.1 All persons in the inventory work must be able to handle and understand all data at level 2.
Data Processing
level 2
7.Transparency DP.2.7.1 Reporting of the calculation principle and equations used.
Data Processing
level 2
7.Transparency DP.2.7.2 Reporting of the theoretical reasoning for all methods
Data Processing
level 2
7.Transparency DP.2.7.3 Reporting of assumptions behind all meth-ods
47
must be given, treating all national emission sources. This documenta-tion still remains to be made, but is planned to be carried out in the fu-ture.
Refer to Section 1.7 in the Danish NIR and the QA/QC sections in the sector chapters.
����������(��3��� ���
Refer to Section 1.7 in the Danish NIR and the QA/QC sections in the sector chapters.
Time-series is prepared and checked, any major change is closely exam-ined with the purpose of verifying and explaining changes from earlier inventories.
Total emission, when aggregated to IPCC and LRTAP reporting tables, is compared with totals based on SNAP source categories (control of data transfer).
Time-series are prepared and checked, any major change is closely exam-ined with the purpose of verifying and explaining fluctuations.
A central documentation will be provided, treating all national emission sources.
�����;�������(�3��� ���
Data Processing
level 2
7.Transparency DP.2.7.4 The reasoning for the choice of methodology for uncertainty analysis needs to written explicitly.
Data Storage
level 3
1. Accuracy DS.3.1.1 Quantification of uncertainty
Data Storage
level 3
5.Correctness DS.3.5.1 Comparison with inventories of the previous years on the level of the categories of the CRF as well as on SNAP source categories. Any major changes are checked, verified, etc.
Data Storage
level 3
5.Correctness DS.3.5.2 Total emissions when aggregated to CRF source categories are compared with totals based on SNAP source categories (control of data transfer).
Data Storage
level 3
5.Correctness DS.3.5.3 Checking of time-series of the CRF and SNAP source categories as they are found in the Corinair databases. Considerable trends and changes are checked and ex-plained.
Data Storage
level 3
7.Transparency DS.3.7.1 Documentation of a correct connection be-tween all data types at DS3 to data at level DS2
Data Processing
level 3
7.Transparency DP.3.7.1 In the calculation sheets, there must be clear Id to Data Storage level 3 data.
48
A central documentation will be provided, treating all national emission sources.
����� ����(��3��� �/�
This PM is checked when the sectoral reports are reviewed by external experts.
For each key source category, a comparison has been made between Denmark and the EU-15 countries. This is performed by comparing emission density indicators, defined as emission intensity value divided by a chosen indicator. The indicators are identical to the ones identified in the Norwegian verification inventory (Holtskog et al., 2000). The cor-relation between emissions and an independent indicator does not nec-essarily imply cause and effect, but in cases where the indicator is di-rectly associated with the emission intensity value, such as for the energy sector, the emission density indicator is a measure of the implied emis-sion factor and a direct comparison can be made. A qualitative verifica-tion of implied emission factors can, furthermore, be made when a measured or theoretical value of the CO2 content in the respective fuel type (or other relevant parameter) is available. For the energy sector, all countries are, in principle, comparable and inter-country deviations arise from variations in fuel purities and fuel combustion efficiencies. A com-parison of national emission density indicators, analogous to the implied emission factors, will give valuable information on the quality and effi-ciency of the national energy sectors.
Furthermore, the inter-country comparison of emission density indica-tors and comparison of theoretical values gives a methodological verifi-cation of the derivation of emission intensity values, and of the correla-tion between emission intensity values and activity values.
When emissions are compared with non-dependent parameters, similari-ties with regard to geography, climate, industry structure and level of economic development may be necessary for obtaining comparable emission density indicators (Thomsen and Fauser, 2006).
This PM is checked when the sectoral reports are reviewed by external experts.
Refer to DS 4.2.1
Data Storage
level 4
1. Accuracy DS.4.1.1 Questionnaire to external experts: The performance of the PMs that relates to accuracy
Data Storage
level 4
2.Comparability DS.4.2.1 Description of similarities and differences in relation to other countries’ inventories for the methodological approach
Data Storage
level 4
3.Completeness DS.4.3.1 Questionnaire to external experts: The performance of the PMs that relate to com-pleteness
Data Storage
level 4
3.Completeness DS.4.3.2 National and international validation includ-ing explanation of the discrepancies.
49
The inventory reporting is in accordance with the UNFCCC guidelines on reporting and review (UNFCCC, 2002). The present report includes detailed and complete information on the inventories for all years from the base year to the year of the current annual inventory submission, in order to ensure the transparency of the inventory. The annual emission inventory for Denmark is reported in the Common Reporting Format (CRF) as requested in the reporting guidelines. The CRF-spreadsheets contain data on emissions, activity data and implied emission factors for each year. Emission trends are given for each greenhouse gas and for to-tal greenhouse gas emissions in CO2 equivalents. The complete sets of CRF-files are available on the NERI homepage (www.dmu.dk).
Data Storage
level 4
7.Transparency DS.4.7.1 External review for evaluation of the com-munication performance
The transparency of the CRF reporting is reviewed by experts when UNFCCC performs annual review of the Danish GHG inventory.
The uncertainty estimates are based on the Tier 1 methodology in the IPCC Good Practice Guidance (GPG) (IPCC, 2000). Uncertainty estimates for the following sectors are included in the current year: stationary combustion plants, mobile combustion, fugitive emissions from fuels, industry, solid waste and wastewater treatment and agriculture. There is a rough estimate of the uncertainty for solvents, the estimate is however not included in this chapter, please refer to the chapter regarding sol-vents. In future reporting solvents will be included in this chapter. The sources included in the uncertainty estimate cover 99.9% of the total Danish greenhouse gas emission (CO2 eq., without CO2 from LULUCF).
The uncertainties for the activity rates and emission factors are shown in Table 1.4.
The estimated uncertainties for total GHG and for CO2, CH4, N2O and F-gases are shown in Table 1.3. The base year for F-gases is 1995 and for all other sources the base year is 1990. The total Danish GHG emission is es-timated with an uncertainty of ±5.4% and the trend in GHG emission since 1990 has been estimated to be -7.5%1 ± 2.2%-age points. The GHG uncertainty estimates do not take into account the uncertainty of the GWP factors.
The uncertainty on N2O from stationary combustion plants, N2O emis-sion from agricultural soils and CH4 emission from manure management are the largest sources of uncertainty for the Danish GHG inventory.
1 Including only emission sources for which the uncertainty has been estimated. LU-LUFC is not included.
Data Storage
level 4
4.Consistency DS.4.4.1 The inventory reporting must follow the international guidelines suggested by UNFCCC and IPCC.
50
The uncertainty of the GHG emission from combustion (sector 1A) is 5.8% and the trend uncertainty is -4.4% ±2.1%-age points.
�� ���� Uncertainty 1990-2005.
The uncertainty estimates include stationary combustion plants, mobile combustion, fugitive emissions from fuels, industry, solid waste and wastewater treatment and agriculture.
1) Uncertainty [%] Trend [%] Uncertainty in trend [%-age points]
CO2 2.3 -4.3 ±1.9
CH4 23 +1.7 ±10.2
N2O 42 -33 ±11.6
F-gases 49 +158 ±64
GHG 5.4 -7.5 ±2.2
51
�� ���� Uncertainty rates for each emission source
IPCC Source category Gas Base year emission
Year t emission Activity data uncertainty
Emission factor uncertainty
Gg CO2 eq Gg CO2 eq % %
Stationary Combustion, Coal CO2 24077 14568 1 5
Stationary Combustion, BKB CO2 11 0 3 5
Stationary Combustion, Coke CO2 138 106 3 5
Stationary Combustion, Petroleum coke CO2 410 859 3 5
Stationary Combustion, Plastic waste CO2 349 685 5 5
Stationary Combustion, Residual oil CO2 2505 1647 2 2
Stationary Combustion, Gas oil CO2 4547 2430 4 5
Stationary Combustion, Kerosene CO2 366 20 4 5
Stationary Combustion, Natural gas CO2 4320 10776 3 1
Stationary Combustion, LPG CO2 169 95 4 5
Stationary Combustion, Refinery gas CO2 806 873 3 5
Stationary combustion plants, gas engines CH4 6 347 2,2 40
Stationary combustion plants, other CH4 115 168 2,2 100
Stationary combustion plants N2O 240 262 2,2 1000
Transport, Road transport CO2 9241 12157 2 5
Transport, Military CO2 119 271 2 5
Transport, Railways CO2 297 232 2 5
Transport, Navigation (small boats) CO2 48 103 42 5
Transport, Navigation (large vessels) CO2 506 440 2 5
Energy, fugitive emissions, oil and natural gas CO2 263 435 15 5
Energy, fugitive emissions, oil and natural gas CH4 40 101 15 50
Energy, fugitive emissions, oil and natural gas N2O 1 2 15 50
6 A. Solid Waste Disposal on Land CH4 1335 1059 10 63
52
6 B. Wastewater Handling CH4 126 253 20 35
6 B. Wastewater Handling N2O 88 61 10 30
2A1 Cement production CO2 882 1456 1 2
2A2 Lime production CO2 152 110 5 5
2A3 Limestone and dolomite use CO2 18 61 5 5
2A5 Asphalt roofing CO2 0 0 5 25
2A6 Road paving with asphalt CO2 2 2 5 25
2A7 Glass and Glass wool CO2 17 13 5 2
2B5 Catalysts/Fertilizers, Pesticides and Sulphuric acid
CO2 1 3 5 5
2C1 Iron and steel production CO2 28 16 5 5
2B2 Nitric acid production N2O 1043 0 2 25
2F Consumption of HFC HFC 218 805 10 50
2F Consumption of PFC PFC 1 14 10 50
2F Consumption of SF6 SF6 107 22 10 50
4A Enteric Fermentation CH4 3110 2630 10 8
4B Manure Management CH4 743 1016 10 100
4B Manure Management N2O 685 557 10 100
4D Agricultural Soils N2O 8308 5677 7,6 19,5
*�4� 0���� ������������.�������� �������
The Danish greenhouse gas emission inventory which was due for sub-mission 15 April 2007 includes all sources identified by the Revised IPPC Guidelines except the following:
Agriculture: The methane conversion factor in relation to the enteric fermentation for poultry and fur farming is not estimated. There is no default value recommended by IPCC (Table A-4 in GPG). However, this emission is seen as non-significant compared with the total emission from enteric fermentation.
LULUCF: Carbon stock changes for “Settlement” and “Other land” is not estimated.
��.�������
Danish Environmental Protection Agency (2007): Ozone depleting sub-stances and the greenhouse gases HFCs, PFCs and SF6. Danish con-sumption and emissions 2005. Tomas Sander Poulsen, PlanMiljø. To be published.
EMEP/CORINAIR, 2004: Emission Inventory Guidebook 3rd edition, prepared by the UNECE/EMEP Task Force on Emissions Inventories and Projections, 2004 update. Available at http://reports.eea.eu.int/E-MEPCORINAIR4/en (15-04-2005)
Illerup, J. B., Lyck, E., Winther, M., and Rasmussen, E. (2000): Denmark’s National Inventory Report – Submitted under the United Na-tions Framework Convention on Climate Change. Emission Inventories. Research Notes from National Environmental Research Institute, Den-mark no. 127, 326 pp. http://www.dmu.dk/1_viden/2_Publikationer-/3_arbrapporter/rapporter/ar127.pdf
IPCC, 1997: Revised 1996 IPCC Guidelines for National Greenhouse Gas Inventories. Available at http://www.ipccnggip.iges.or.jp/public/gl/i-nvs6.htm (15-04-2007).
IPCC, 2000: Good Practice Guidance and Uncertainty Management in National Greenhouse Gas Inventories. Available at http://www.ipcc-nggip.iges.or.jp/public/gp/english/ (15-04-2007).
Mikkelsen M.H., Gyldenkærne, S., Poulsen, H.D., Olesen, J.E. & Sommer, S.G. 2006: Emission of ammonia, nitrous oxide and methane from Danish Agriculture 1985 – 2002. Methodology and Estimates. National Envi-ronmental Research Institute, Denmark. 90 pp – Research Notes from NERI No. 231. http://www.dmu.dk/Pub/AR231.pdf.
Pulles, T., Mareckova, K., Svetlik, J., Linek, M., and Skakala, J. (1999): CollectER -Installation and User Guide, EEA Technical Report No 31. http://reports.eea.eu.int/binaryttech31pdf/en
Ntziachristos, L., Samaras, Z. (2000): COPERT III Computer Programme to Calculate Emissions from Road Transport - Methodology and Emis-sion Factors (Version 2.1). Technical report No 49. European Environ-ment Agency, November 2000, Copenhagen. http://reports.eea.eu.int-/Technical_report_No_49/en
Holtskog, S., Haakonsen, G., Kvingedal, E., Rypdal, K. & Tornsjø, B., 2000. Verification of the Norwegian emission inventory – Comparing emission intensity values with similar countries. The Norwegian Pollu-tion Control Authority in cooperation with Statistics Norway. SFT report 1736/2000.
Sørensen, P.B., Illerup, J.B., Nielsen, M., Lyck, E., Bruun, H.G., Winther, M., Mikkelsen, M.H. & Gyldenkærne, S. (2005): Quality manual for the green house gas inventory. Version 1. National Environmental Research Institute. - Research Notes from NERI 224: 25 pp. (electronic). http://www2.dmu.dk/1_viden/2_Publikationer/3_arbrapporter/rapporter/AR224.pdf
Thomsen, M. & Fauser, P. 2007. Verification of the Danish emission in-ventory data by national and international data comparisons. NERI working report. To be published.
54
UNFCCC (2002): GUIDELINES ON REPORTING AND REVIEW OF GREENHOUSE GAS INVENTORIES. http://unfccc.int/resource/docs/2002/sbsta/misc11.pdf
0����!���0������������The greenhouse gas emissions are estimated according to the IPCC guidelines and are aggregated into seven main sectors. The greenhouse gases include CO2, CH4, N2O, HFCs, PFCs and SF6. Figure 2.1 shows the estimated total greenhouse gas emissions in CO2 equivalents from 1990 to 2005. The emissions are not corrected for electricity trade or tempera-ture variations. CO2 is the most important greenhouse gas, followed by N2O and CH4 in relative importance. The contribution to national totals from HFCs, PFCs and SF6 is approximately 1%. Stationary combustion plants, transport and agriculture represent the largest sources. The net CO2 removal by forestry and soil (Land Use and Land Use Change and Forestry (LULUCF)) is in the region of 2 % of the total emission in CO2 equivalents in 2005. The national total greenhouse gas emission in CO2 equivalents without LUCF has decreased by 7 % from 1990 to 2005 and by 10 % with LULUCF.
�������� Greenhouse gas emissions in CO2 equivalents distributed on main sectors for 2005 and time-series for 1990 to 2005.
'��"��#��-�#��The largest source to the emission of CO2 is the energy sector, which in-cludes combustion of fossil fuels like oil, coal and natural gas (Figure 2.2). Energy Industries contribute with 44 % of the emissions. About 26 % come from the transport sector. The CO2 emission decreased by ap-proximately 7 % from 2004 to 2005. The reason for this decrease was mainly due to import of electricity. Also higher outdoor temperature in 2005 compared with 2004 contributed to the decrease. If the CO2 emis-sion is adjusted for climatic variations and electricity trade with other countries the CO2 emission from combustion of fossil fuels has decreased by about 16 % since 1990. The decrease in CO2 emissions is observed de-
Energy andtransportation
78,3%
Agriculture15,5%
Solvents0,2%
Industrialprocesses
3,9%
Waste2,1%
0100002000030000400005000060000700008000090000
100000
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
���������������� ����� CO2
CH4
N2O
HFC’s,PFC’s, SF6
Total
Total withoutLUCF
56
spite increases in the gross energy consumption of 3.2 % and in the gross national product of 39 %. This is due to change of fuel types from coal to natural gas and renewable energy. As a result of the lower consumption of coal in recent years, the main part of the CO2 emission comes from oil combustion. In 2005, the actual CO2 emission was about 4 % lower than the emission in 1990.
��������� CO2 emissions. Distribution according to the main sectors (2005) and time-series for 1990 to 2005.
�����!���-�#��Agriculture is the most important N2O emission source (Figure 2.3). As a result of microbial processes N2O is emitted in the soil and in animal manure). Substantial emissions also come from drainage water and coastal waters where nitrogen is converted to N2O through bacterial processes. However, the nitrogen converted in these processes originates mainly from the agricultural use of manure and fertilisers. The main rea-son for the drop in the emissions of approximately 33 % from 1990 to 2005 is legislation to improve the utilisation of nitrogen in manure. The legislation has resulted in less nitrogen excreted per unit of livestock produced and a considerable reduction in the use of fertilisers. The basis for the N2O emission is then reduced. Approximately 11 % of the emis-sion of N2O comes from combustion of fossil fuels, and transport ac-counts for around 6 %. The N2O emission from transport has increased during the nineties because of the increase in the use of catalyst cars. Production of nitric acid stopped in 2004 and the emissions from indus-trial processes is therefore zero in 2005.
��������� N2O emissions. Distribution according to the main sectors (2005) and time-series for 1990 to 2005.
Energy Industries
44%
Other Sectors14%
Other2%
Industrial Processes
3%
Transport26%
Manufacturing Industries and Construction
11%
0
10000
20000
30000
40000
50000
60000
70000
80000
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
CO
2 em
issi
on
(10
00 to
nnes
)
Energy Industries
ManufacturingIndustries andConstructionTransport
Other Sectors
Industrial Processes
Other
Total
Total, adjusted
Agriculture89%
Energy11%
0
5
10
15
20
25
30
35
40
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
N2O
em
issi
on (
1000
ton
nes)
Energy
Industrial Processes
Agriculture
Total
57
A�����The largest sources of anthropogenic CH4 emissions are agricultural ac-tivities, managed waste disposal on land, public power and district heat-ing plants (Figure 2.4). The emission from agriculture derives from en-teric fermentation and management of animal manure. The increasing CH4 emissions from public power and district heating plants are due to the increasing use of gas engines in the decentralised cogeneration plant sector. Up to 3 % of the natural gas in the gas engines is not combusted. From 1990, the emission of CH4 from enteric fermentation has decreased due to the decrease in the number of cattle. However, the emission from manure management has increased due to a change in traditional stable systems towards an increase in slurry-based stable systems. Altogether, the emission of CH4 for the agriculture sector has decreased by approxi-mately 9 % from 1990 to 2005. The emission of CH4 from waste disposal has decreased slightly due to increases in the incineration of waste.
��������� CH4 emissions. Distribution according to the main sectors (2005) and time-series for 1990 to 2005.
B%'�)�;%'���#� %��This part of the Danish inventory only comprises data for all substances from 1995. From 1995 to 2000, there has been a continuous and substan-tial increase in the contribution from the range of F-gases as a whole, cal-culated as the sum of emissions in CO2 equivalents (Figure 2.5). This in-crease is simultaneous with the increase in the emission of HFCs. For the time-series 2000-2005, the increase has been much lower than for the years 1995 to 2000. SF6 contributed considerably in earlier years, with about 50 % in 1993. Environmental awareness and regulation of this gas under Danish law has reduced its use in industry, with the result that the contribution in 2005 was approximately 3 %. The use of HFCs, and espe-cially HFC-134a as a major contributor to HFCs, has increased several fold. HFCs have, therefore, become dominant F-gases, comprising about 50 % in 1993, but 96 % in 2005. HFC-134a is mainly used as a refrigerant. However, the use of HFC-134a as a refrigerant, as well as the use of other HFCs as refrigerants, is stable or falling. This is due to Danish legislation, which, in 2007, forbids new HFC-based refrigerant stationary systems. On the other hand, the use of air conditioning in mobile systems is on the increase.
Manure Management18%
Waste23%
Other7%
Energy Industries5%
Enteric Fermentation47%
0
50
100
150
200
250
300
350
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
CH
4 em
issi
on
(100
0 to
nne
s)Energy Industries
Enteric Fermentation
Manure Management
Waste
Other
Total
58
��������� F-gas emissions. Time-series for 1990 to 2005.
���(��The emission of CO2 from Energy Industries has decreased by approxi-mately 15 % from 1990 to 2005. The relatively large fluctuation in the emission is due to inter-country electricity trade. Thus, the high emis-sions in 1991, 1996 and 2003 reflect a large electricity export and the low emissions in 1990 and 2005 are due to a large import of electricity. The increasing emission of CH4 is due to the increasing use of gas engines in decentralised cogeneration plants. The CO2 emission from the transport sector increased by 26 % since 1990, mainly due to increasing road traffic.
+(��������The agricultural sector contributes with 16 % of the total greenhouse gas emission in CO2 equivalents and is one of the most important sectors re-garding the emissions of N2O and CH4. In 2005, the contribution of N2O and CH4 to the total emission was 89 % and 65%, respectively. The N2O emission decreased by 31% and the CH4 emission by 9 % from 1990 to 2005.
�"����������������The emissions from industrial process, i.e. emissions from processes other than fuel combustion, amount to about 4 % of the total emission in CO2 equivalents. The main sources are cement production, refrigeration, foam blowing and calcination of limestone. The CO2 emission from ce-ment production – which is the largest source contributing with almost 3 % of the national total – increased by 65% from 1990 to 2005. The second largest source has been N2O from the production of nitric acid. However, the production of nitric acid/fertiliser ceased in 2004 and therefore the emission of N2O is zero in 2005.
$�����Waste contributes in 2005 with 23 % of the CH4 emission and the emis-sion has decreased by 21 % from 1990 to 2005. In 2005 the contribution from waste disposal was 19 % of the total CH4 emission. The decrease is due to the increasing use of waste for power and heat production. Since all incinerated waste is used for power and heat production, the emis-sions are included in the 1A1a IPCC category. The CH4 emission from wastewater handling amounts to about 5 % of the total CH4 emissions.
0
100
200
300
400
500
600
700
800
900
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005C
O2 -
equi
v., F
-gas
es (1
000
tonn
es)
HFCs
PFCs
SF6
Total
59
&�����The annual C-stock change for forest land remaining forest land in 2005 were reduced from app. 3300 Gg CO2 in 2004 to 1600 Gg CO2 in 2005 due to storms. As no storms occurred in 2006 it is expected that this figure will increase in the next reporting. The C sequestration in afforested stands increased again in 2005 and will continue to do so over the com-ing decades due to i) increasing growth rates as afforested stands grow older and ii) an increase in the total area under afforestation.
.�����" �(������"���"�/�����"��The emission from cropland, grassland and wetlands are nearly the same in 2005 as in 2004 or equivalent to a net carbon emission of 370 Gg CO2. The major source is from organic agricultural soils (1078 Gg CO2). Min-eral soils are estimated to have a carbon sequestration of 897 Gg CO2 in 2005. From 2004 to 2005 there has been a slightly increase in the carbon sequestration in mineral soils. A continuous increase in raised number of shelterbelts increases the C sequestration here. The emission estimates from mineral soils is very variable across the years due to variations in yield level and annual temperatures, which affect the degradation rate of organic matter in the applied Tier 3 model.
The largest sources of emissions of NOX are other mobile sources fol-lowed by road transport and combustion in energy industries (mainly public power and district heating plants). The transport sector is the sec-tor contributing the most to the emission of NOX and, in 2005, 35% of the Danish emissions of NOX stems from road transport, national navigation, railways and civil aviation. Also emissions from national fishing and off-road vehicles contribute significantly to the NOX emission. For non-industrial combustion plants, the main sources are combustion of gas oil, natural gas and wood in residential plants. The emissions from public power plants and district heating plants have decreased by 61 % from 1985 to 2005. In the same period, the total emission decreased by 36%. The reduction is due to the increasing use of catalyst cars and installation of low-NOX burners and denitrifying units in power and district heating plants.
��������� NOX emissions. Distribution according to the main sectors (2005) and time-series for 1990 to 2005.
Fugitive emissions from
fuels1%
Transport45%
Manufacturing industries andConstruction
12%
Energy industries26%
Other sectors16%
0
50000
100000
150000
200000
250000
300000
350000
1985
1987
1989
1991
1993
1995
1997
1999
2001
2003
2005
NO
x em
issi
ons
(tonn
es)
Energy industries
Manufacturing industriesand ConstructionTransport
Other sectors
Fugitive emissions fromfuelsIndustrial Processes
Agriculture
Solid waste disposal onlandTotal
Total
60
'6�Transport is responsible for the dominant share of the total CO emission. Also other mobile sources and non-industrial combustion plants con-tribute significantly to the total emission of this pollutant. The drop in the emissions seen in 1990 was a consequence of a law forbidding the burning of agricultural waste on fields. The emission decreased further by 21 % from 1990 to 2005, largely because of decreasing emissions from road transportation.
��������� CO emissions. Distribution according to the main sectors (2005) and time-series for 1990 to 2005.
�AC6'�The emissions of NMVOC originate from many different sources and can be divided into two main groups: incomplete combustion and evaporation. Road vehicles and other mobile sources such as national navigation vessels and off-road machinery are the main sources of NMVOC emissions from incomplete combustion processes. Road trans-portation vehicles are still the main contributors, even though the emis-sions have declined since the introduction of catalyst cars in 1990. The evaporative emissions mainly originate from the use of solvents. The emissions from the energy industries have increased during the nineties due to the increasing use of stationary gas engines, which have much higher emissions of NMVOC than conventional boilers. The total an-thropogenic emissions have decreased by 31 % from 1985 to 2005, largely due to the increased use of catalyst cars and reduced emissions from use of solvents.
��������� NMVOC emissions. Distribution according to the main sectors (2005) and time-series for 1990 to 2005.
Manufacturing industries andConstruction
3%
Energy industries2%
Other sectors63%
Transport32%
0
200.000
400.000
600.000
800.000
1.000.000
1.200.000
1985
1987
1989
1991
1993
1995
1997
1999
2001
2003
2005
CO
em
issi
ons
(ton
nes)
Transport
Othersectors
Agriculture
Total
Transport22%
Other sectors26%
IndustrialProcesses
1%
Fugitive emissions from
fuels13%
Manufacturing industries andConstruction
2%
Energy industries3%Agriculture
1%
Solvent and otherproduct use
32%
0
20000
40000
60000
80000
100000
120000
140000
160000
180000
200000
1985
1987
1989
1991
1993
1995
1997
1999
2001
2003
2005
NM
VO
C e
mis
sion
s (to
nnes
)
Transport
Other sectors
Fugitiveemissions fromfuelsIndustrialProcesses
Solvent andother productuseAgriculture
Total
61
6��The main part of the SO2 emission originates from combustion of fossil fuels, i.e. mainly coal and oil, in public power and district heating plants. From 1980 to 2005, the total emission decreased by 95 %. The large re-duction is largely due to installation of desulphurisation plant and use of fuels with lower content of sulphur in public power and district heating plants. Despite the large reduction of the SO2 emissions, these plants make up 36 % of the total emission. Also emissions from industrial com-bustion plants, non-industrial combustion plants and other mobile sources are important. National sea traffic (navigation and fishing) con-tributes with about 12 % of the total SO2 emission. This is due to the use of residual oil with high sulphur content.
��������� SO2 emissions. Distribution according to the main sectors (2005) and time-series for 1990 to 2005.
Fugitive emissions from
fuels1%
Manufacturing industries andConstruction
27%
Other sectors23%
Transport10%
Energyindustries
36%
IndustrialProcesses
2%
0
50000
100000
150000
200000
250000
300000
350000
400000
450000
500000
1979
1981
1983
1985
1987
1989
1991
1993
1995
1997
1999
2001
2003
2005
SO
2 em
issi
ons
(tonn
es)
Energy industries
Manufacturing industriesand Construction
Transport
Other sectors
Total
62
�� ���(��1'�%��������*2�
��*� 6������&��.�����������
The energy sector has been reported in four main chapters:
3.2 Stationary combustion plants (CRF sector 1A1, 1A2 and 1A4) 3.3 Transport (CRF sector 1A2, 1A3, 1A4 and 1A5) 3.4 Additional information on fuel combustion (CRF sector 1A) 3.5 Fugitive emissions (CRF sector 1B)
Though industrial combustion is part of stationary combustion, detailed documentation for some of the specific industries is discussed in the in-dustry chapters. Table 3.1 shows detailed source categories for the en-ergy sector and plant category in which the sector is discussed in this re-port.
63
�� ���� CRF energy sectors and relevant NIR chapters
IPCC id IPCC sector name NERI documentation
1 Energy Stationary combustion, Transport, Fugitive, Industry
1A Fuel Combustion Activities Stationary combustion, Transport, Industry
1A1 Energy Industries Stationary combustion
1A1a Electricity and Heat Production Stationary combustion
1A1b Petroleum Refining Stationary combustion
1A1c Solid Fuel Transf./Other Energy Industries Stationary combustion
1A2 Fuel Combustion Activities/Industry (ISIC) Stationary combustion, Transport, Industry
1A2a Iron and Steel Stationary combustion, Industry
1A2b Non-Ferrous Metals Stationary combustion, Industry
1A2c Chemicals Stationary combustion, Industry
1A2d Pulp, Paper and Print Stationary combustion, Industry
1A2e Food Processing, Beverages and Tobacco Stationary combustion, Industry
1A2f Other (please specify) Stationary combustion, Transport, Industry
1A3 Transport Transport
1A3a Civil Aviation Transport
1A3b Road Transportation Transport
1A3c Railways Transport
1A3d Navigation Transport
1A3e Other (please specify) Transport
1A4 Other Sectors Stationary combustion, Transport
Fuel consumption and emissions from stationary combustion plants in CRF sectors 1A1, 1A2 and 1A4 are all included in this chapter. Further details on the inventories for stationary combustion are enclosed in An-nex 3A.
1. Energy Industries 0,48 0,51 0,52 0,55 0,50 0,46
2. Manufacturing Industries and Construction 0,19 0,19 0,18 0,18 0,19 0,18
3. Transport 1,22 1,23 1,29 1,35 1,40 1,43
4. Other Sectors 0,31 0,32 0,32 0,33 0,32 0,33
5. Other 0,00 0,01 0,00 0,00 0,01 0,01
B. Fugitive Emissions from Fuels 0,01 0,01 0,01 0,01 0,01 0,01
1. Solid Fuels 0 0 0 0 0 0
2. Oil and Natural Gas 0,01 0,01 0,01 0,01 0,01 0,01
66
����*� �!��������(����#����������
Emission source categories, fuel consumption data and emission data are presented in this chapter.
����������!��������(������In the Danish emission database, all activity rates and emissions are de-fined in SNAP sector categories (Selected Nomenclature for Air Pollu-tion) according the CORINAIR system. The emission inventories are prepared from a complete emission database, based on the SNAP sec-tors. Aggregation to the IPCC sector codes is based on a correspondence list between SNAP and IPCC enclosed in Annex 3A. Stationary combus-tion is defined as combustion activities in the SNAP sectors 01-03.
Stationary combustion plants are included in the emission source sub-categories:
• 1A1 Energy, Fuel consumption, Energy Industries • 1A2 Energy, Fuel consumption, Manufacturing Industries and Con-
struction • 1A4 Energy, Fuel consumption, Other Sectors The emission sources ��� and ���2 however also include emissions from transport subsectors. The emission source ��� includes emissions from some off-road machinery in the industries. The emission source ��� in-cludes off-road machinery in agriculture/forestry and house-hold/gardening. Further emissions from national fishing are included in subsector ���.
The emission and fuel consumption data presented in tables and figures in Chapter 3.2 only includes emissions originating from stationary com-bustion plants of a given IPCC sector. The IPCC sector codes have been applied unchanged, but some sector names have been changed to reflect the stationary combustion element of the source.
%!� ����!������In 2005, the total fuel consumption for stationary combustion plants was 531 PJ of which 424 PJ was fossil fuels.
Fuel consumption distributed according to the stationary combustion subsectors is shown in Figure 3.1 and Figure 3.2. The majority - 57% - of all fuels is combusted in the sector, $�"#��� �#������������������*���������� Other sectors with high fuel consumption are � �������# and ��� ��.
67
�������� Fuel consumption rate of stationary combustion, 2005 (based on DEA 2006a)
Coal and natural gas are the most utilised fuels for stationary combus-tion plants. Coal is mainly used in power plants and natural gas is used in power plants and decentralised CHP plants, as well as in industry, district heating and households.
��������� Fuel consumption of stationary combustion plants 2005 (based on DEA 2006a)
Fuel consumption time-series for stationary combustion plants are pre-sented in Figure 3.3. The total fuel consumption increased by 6.6% from 1990 to 2005, while the fossil fuel consumption decreased by 4.9%. The consumption of natural gas and renewable fuels has increased since 1990, whereas the consumption of coal has decreased.
The fuel consumption rate fluctuates considerably, largely due to elec-tricity import/export but also due to outdoor temperature variations. The fuel consumption fluctuation is further discussed in the chapter “Emissions”.
Fuel consumption including renewable fuels Fuel consumption, fossil fuels
1A1a Public electricity and heat production57%
1A1b Petroleum refining3%
1A1c Other energy industries5%
1A2f Industry14%
1A4a Commercial / Institutional3%
1A4b Residential16%
1A4c Agriculture / Forestry / Fisheries2%
1A4c Agriculture / Forestry / Fisheries2%
1A4b Residential13%
1A4a Commercial / Institutional3%
1A2f Industry16%
1A1c Other energy industries7%
1A1b Petroleum refining4%
1A1a Public electricity and heat production55%
0
20
40
60
80
100
120
140
160
180
200
CO
AL
CO
KE
OV
EN
CO
KE
PE
TR
OLE
UM
CO
KE
WO
OD
AN
D S
IMIL
.
MU
NIC
IP. W
AS
TE
S
ST
RA
W
SE
WA
GE
SLU
DG
E
RE
SID
UA
L O
IL
GA
S O
IL
KE
RO
SE
NE
NA
TU
RA
L G
AS
LPG
RE
FIN
ER
Y G
AS
BIO
GA
S
Fue
l con
sum
ptio
n [P
J]
1A4c Agriculture /Forestry /Fisheries
1A4b Residential
1A4a Commercial/ Institutional
1A2f Industry
1A1c Otherenergy industries
1A1b Petroleumrefining
1A1a Publicelectricity and heatproduction
68
��������� Fuel consumption time-series, stationary combustion (based on DEA 2006a)
���������The GHG emissions from stationary combustion are listed in Table 3.5. The emission from stationary combustion accounts for 52% of the total Danish GHG emission.
The CO2 emission from stationary combustion plants accounts for 64% of the total Danish CO2 emission (not including land-use change and for-estry). The CH4 emission from stationary combustion accounts for 9% of the total Danish CH4 emission and the N2O emission from stationary combustion accounts for 4% of the total Danish N2O emission.
�� ����� Greenhouse gas emission for the year 2005 1).
CO2 CH4 N2O
Gg CO2 equivalent
1A1 Fuel consumption, Energy industries 22130 292 142
1A2 Fuel consumption, Manufacturing Indus-tries and Construction1)
4621 27 43
1A4 Fuel consumption, Other sectors 1) 5306 196 77
Total emission from stationary combustion plants
32058 515 262
Total Danish emission (gross) 50426 5636 7044
%
Emission share for stationary combustion 64 9 4
1) Only stationary combustion sources of the sector is included
CO2 is the most important GHG pollutant and accounts for 97.7% of the GHG emission (CO2 eqv.).
0
100
200
300
400
500
600
700
800
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
Fue
l con
sum
ptio
n [P
J]
Otherbiomass
Waste,biomass part
Other fossilfuels
Gas oil
Residual oil
Natural gas
Coal, browncoal and coke
69
Stationary combustion
CH4
1,2%N2O1,0%
CO2
97,7%
��������� GHG emission (CO2 equivalent) from stationary combustion plants
Figure 3.5 depicts the time-series of GHG emission (CO2 eqv.) from sta-tionary combustion and it can be seen that the GHG emission develop-ment follows the CO2 emission development very closely. Both the CO2 and the total GHG emission are lower in 2005 than in 1990 – CO2 by 15% and GHG by 14%. However, fluctuations in the GHG emission level are large.
��������� GHG emission time-series for stationary combustion
The fluctuations in the time-series are largely a result of electricity im-port/export activity, but also of outdoor temperature variations from year to year. These fluctuations are shown in Figure 3.6. The fluctuations follow the fluctuations in fuel consumption.
In 1990, the Danish electricity import was large causing relatively low fuel consumption, whereas the fuel consumption was high in 1996 due to a large electricity export. In 2005 the net electricity import was 4932 TJ in previous years there had been a net export. The electricity import in 2005 was a result of heavy rainfall in Norway and Sweden causing large hy-dropower production in both countries.
0
10
20
30
40
50
60
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
GH
G [
Tg
CO
2 eq
.]
Total
CO2
CH4N2O
70
To be able to follow the national energy consumption, and for statistical and reporting purposes, the Danish Energy Authority produces a correc-tion of the actual emissions without random variations in electricity im-ports/exports and in ambient temperature. This emission trend, which is smoothly decreasing, is also illustrated in Figure 3.6. The corrections are included here to explain the fluctuations in the emission time-series. The GHG emission corrected for electricity import/export and ambient tem-perature has decreased by 23% since 1990, and the CO2 emission by 25%.
71
��������� GHG emission time-series for stationary combustion and adjustment for electricity import/export and temperature variations (DEA 2006b)
Degree days Fuel consumption adjusted for electricity trade
0
500
1000
1500
2000
2500
3000
3500
4000
450019
85
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
Deg
ree
days
0
100
200
300
400
500
600
700
800
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
Fue
l con
sum
ptio
n [P
J]
Otherbiomass
Waste,biomass part
Other fossilfuels
Gas oil
Residual oil
Natural gas
Coal, browncoal and coke
Electricity trade Fluctuations in electricity trade compared to fuel consumption
-40
-30
-20
-10
0
10
20
30
40
50
60
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
Ele
ctric
ity im
port
[PJ]
0
100
200
300
400
500
600
700
800
1985
1987
1989
1991
1993
1995
1997
1999
2001
2003
2005
Fue
l con
sum
ptio
n
-60
-40
-20
0
20
40
60
80
100
����������������
Fossil fuel consumption [PJ]
Coal consumption [PJ]
Electricity export [PJ]
Fuel consumption adjustment as a result of electricity trade GHG emission
-150
-100
-50
0
50
100
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
Adj
ustm
ent o
f fue
l con
sum
ptio
n [P
J]
0
10
20
30
40
50
60
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
GH
G [
Tg
CO
2 eq
.]
Total
CO2
CH4N2O
CO2 emission adjustment as a result of electricity trade Adjusted GHG emission, stationary combustion plants
-15
-10
-5
0
5
10
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
Adj
ustm
ent o
f CO
2 em
issi
on [G
g]
0
10
20
30
40
50
60
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
GH
G [
Tg
CO
2 eq
.]
Total
CO2
CH4N2O
72
'6��The CO2 emission from stationary combustion plants is one of the most important GHG emission sources. Thus the CO2 emission from station-ary combustion plants accounts for 64% of the total Danish CO2 emis-sion. Table 3.6 lists the CO2 emission inventory for stationary combus-tion plants for 2005. Figure 3.7 reveals that �#������������������*��������� accounts for 61% of the CO2 emission from stationary combustion. This share is somewhat higher than the fossil fuel consumption share for this sector, which is 55% (Figure 3.1). Other large CO2 emission sources are industrial plants and residential plants. These are the sectors, which also account for a considerable share of fuel consumption.
�� ����� CO2 emission from stationary combustion plants 20051)
CO2 2005
1A1a Public electricity and heat production 19606 Gg
1A1b Petroleum refining 932 Gg
1A1c Other energy industries 1593 Gg
1A2 Industry 4621 Gg
1A4a Commercial / Institutional 911 Gg
1A4b Residential 3712 Gg
1A4c Agriculture / Forestry / Fisheries 683 Gg
Total 32058 Gg
1) Only emission from stationary combustion plants in the sectors is included
��������� CO2 emission sources, stationary combustion plants, 2005
The sector �#������������������*��������� consists of the SNAP source sec-tors: $�"#��� *�-�� and 1� ������ �������. The CO2 emissions from each of these subsectors are listed in Table 3.7. The most important subsector is power plant boilers >300MW.
1A1b Petroleum refining3%
1A1c Other energy industries5%
1A2 Industry14%
1A4b Residential12%
1A4a Commercial / Institutional3%
1A4c Agriculture / Forestry / Fisheries2%
1A1a Public electricity and heat production61%
73
�� ����� CO2 emission from subsectors to ���������������� �������� �����.
The CO2 emission from combustion of biomass fuels is not included in the total CO2 emission data, because biomass fuels are considered CO2 neutral. The CO2 emission from biomass combustion is reported as a memo item in the Climate Convention reporting. In 2005, the CO2 emis-sion from biomass combustion was 10615 Gg.
Time-series for CO2 emissions are provided in Figure 3.8. Despite an in-crease in fuel consumption of 6.6% since 1990, CO2 emission from sta-tionary combustion has decreased by 15% due to the change in the type of fuels used.
The fluctuations of CO2 emission are discussed earlier.
��������� CO2 emission time-series for stationary combustion plants
'B��CH4 emission from stationary combustion plants accounts for 9% of the total Danish CH4 emission. Table 3.8 lists the CH4 emission inventory for stationary combustion plants in 2005. Figure 3.9 reveals that �#������������������*��������� accounts for 57% of the CH4 emission from stationary combustion, this being closely aligned with the fuel consumption share.
The CH4 emission factor for reciprocating lean-burn gas engines is much higher than for other combustion plants due to the continuous igni-tion/burn-out of the gas. Lean-burn gas engines have an especially high emission factor as discussed in the chapter regarding emission factors. A considerable number of lean-burn gas engines are in operation in Den-mark and these plants account for 67% of the CH4 emission from station-ary combustion plants (Figure 3.10). The engines are installed in CHP plants and the fuel used is either natural gas or biogas.
Gas engines67%
Other stationary combustion plants33%
��������� Gas engine CH4 emission share, 2005.
75
The CH4 emission from stationary combustion increased by a factor of 4.2 since 1990 (Figure 3.11). This is due to the considerable number of lean-burn gas engines installed in CHP plants in Denmark in this period. This increase is also the reason for the increasing IEF (implied emission factor) for gaseous fuels and biomass in the CRF sectors ���, ��� and ���. Figure 3.12 provides time-series for the fuel consumption rate in gas engines and the corresponding increase in CH4 emission.
�������� CH4 emission time-series for stationary combustion plants
0
5
10
15
20
25
30
35
40
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
Fue
l con
sum
ptio
n [P
J]
Gas engines, Natural gas Gas engines, Biogas
0
5
10
15
20
25
30
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
CH
4 em
issi
on [G
g]
Gas engines Other stationary combustion plants
��������� Fuel consumption and CH4 emission from gas engines, time-series.
0
5
10
15
20
25
30
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
CH
4 [G
g]
1A1a Publicelectricity and heatproduction1A1b Petroleumrefining
1A1c Other energyindustries
1A2 Industry
1A4a Commercial /Institutional
1A4b Residential
1A4c Agriculture /Forestry / Fisheries
Total
Total
76
��6�The N2O emission from stationary combustion plants accounts for 4% of the total Danish N2O emission. Table 3.9 lists the N2O emission inven-tory for stationary combustion plants in the year 2005. Figure 3.13 re-veals that �#��������� ���� ����� *��������� accounts for 43% of the N2O emission from stationary combustion. This is lower than the fuel con-sumption share.
�� ����� N2O emission from stationary combustion plants 2005 1)
N2O 2005
1A1a Public electricity and heat production 364 Mg
1A1b Petroleum refining 33 Mg
1A1c Other energy industries 61 Mg
1A2 Industry 140 Mg
1A4a Commercial / Institutional 24 Mg
1A4b Residential 197 Mg
1A4c Agriculture / Forestry / Fisheries 26 Mg
Total 846 Mg
1) Only emission from stationary combustion plants in the sectors is included
Figure 3.14 shows the time-series for the N2O emission. The N2O emis-sion from stationary combustion increased by 9% from 1990 to 2005, but, again, fluctuations in emission level due to electricity import/export are considerable.
77
��������� N2O emission time-series for stationary combustion plants
6�)��6�)��AC6'��#�'6�The emissions of SO2, NOX, NMVOC and CO from Danish stationary combustion plants 2005 are presented in Table 3.10. Further details are shown in Annex 3A. SO2 from stationary combustion plants accounts for 84% of the total Danish SO2 emission. NOX, CO and NMVOC account for 37%, 45% and 20%, respectively, of the total Danish emissions for these substances.
�� ����� SO2, NOX, NMVOC and CO emission from stationary combustion plants 2005
1) Only emissions from stationary combustion plants in the sectors are included
������ A���#� �(��� ����!���
The Danish emission inventory is based on the CORINAIR (COoRdina-tion of INformation on AIR emissions) system, which is a European pro-gramme for air emission inventories. CORINAIR includes methodology structure and software for inventories. The methodology is described in the EMEP/CORINAIR Emission Inventory Guidebook 3rd edition, pre-pared by the UNECE/EMEP Task Force on Emissions Inventories and Projections (EMEP/CORINAIR, 2004). Emission data are stored in an Access database, from which data are transferred to the reporting for-mats.
0,0
0,2
0,4
0,6
0,8
1,0
1,2
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
N2O
[Gg]
1A1a Publicelectricity and heatproduction1A1b Petroleumrefining
1A1c Other energyindustries
1A2 Industry
1A4a Commercial /Institutional
1A4b Residential
1A4c Agriculture /Forestry / Fisheries
Total
Total
Pollutant NOX
Gg
CO
Gg
NMVOC
Gg
SO2
Gg
1A1 Fuel consumption, Energy industries 47.9 11.2 3.8 8.1
1A2 Fuel consumption, Manufacturing industries and Construction (Stationary combustion) 12.5 12.4 0.6 6.0
Total emission from stationary combustion plants 68.5 274.0 23.6 18.3
Total Danish emission 185.8 611.2 118.3 21.9
%
Emission share for stationary combustion 37 45 20 84
78
The emissions inventory for stationary combustion is based on activity rates from the Danish energy statistics. General emission factors for vari-ous fuels, plants and sectors have been determined. Some large plants, such as power plants, are registered individually as large point sources and plant-specific emission data is used.
3��(���������!�����Large emission sources such as power plants, industrial plants and refin-eries are included as large point sources in the Danish emission database. Each point source may consist of more than one part, e.g. a power plant with several units. By registering the plants as point sources in the data-base, it is possible to use plant-specific emission factors.
In the inventory for the year 2005, 75 stationary combustion plants are specified as large point sources. These point sources include:
• Power plants and decentralised CHP plants (combined heat and power plants)
• Municipal waste incineration plants • Large industrial combustion plants • Petroleum refining plants The criteria for selection of point sources consist of the following:
• All centralised power plants, including smaller units. • All units with a capacity above 25 MWe • All district heating plants with an installed effect of 50 MW or above
and a significant fuel consumption • All waste incineration plants included in the Danish law with regard
to the preparation of “green accounts” ”3�,�����4��# �� �+� )� �� #� ��%)��, �+����� �*#������#��������"�(�����4������� ,�"”.
• Industrial plants • with an installed effect of 50 MW or above and significant fuel
consumption. • with a significant process-related emission.
The fuel consumption of stationary combustion plants registered as large point sources is 341 PJ (2005). This corresponds to 64% of the overall fuel consumption for stationary combustion.
Further details regarding the large point sources are provided in Annex 3A. The number of large point sources registered in the databases in-creased from 1990 to 2005.
The emissions from a point source are based either on plant-specific emission data or, if plant specific data are not available, on fuel con-sumption data and the general Danish emission factors.
SO2 and NOX emissions from large point sources are often plant-specific, based on emission measurements. Emissions of CO and NMVOC are also plant-specific for some plants. Plant-specific emission data are ob-tained from:
• Annual environmental reports
79
• Annual plant-specific reporting of SO2 and NOX from power plants >25MWe prepared for the Danish Energy Authority due to Danish legislatory requirements
• Emission data reported by Elsam and E2, the two major electricity suppliers
• Emission data reported from industrial plants. Annual environmental reports for the plants include a considerable number of emission datasets. Emission data from annual environmental reports are, in general, based on emission measurements, but some emis-sions have potentially been calculated from general emission factors.
If plant-specific emission factors are not available, general area source emission factors are used. Emissions of the greenhouse gases (CO2, CH4 and N2O) from the large point sources are all based on area source emis-sion factors.
7������!�����Fuels not combusted in large point sources are included as sector-specific area sources in the emission database. Plants such as residential boilers, small district heating plants, small CHP plants and some industrial boil-ers are defined as area sources. Emissions from area sources are based on fuel consumption data and emission factors. Further information on emission factors is provided below.
7�������������)�.!� ����!������The fuel consumption rates are based on the official Danish energy statis-tics prepared by the Danish Energy Authority (DEA). The DEA aggre-gates fuel consumption rates to SNAP sector categories (DEA 2006a). Some fuel types in the official Danish energy statistics are added to ob-tain a less detailed fuel aggregation level, see Annex 3A. The calorific values on which the energy statistics are based are also included in the annex.
The fuel consumption of the IPCC sector ������������������ ���� ���� �������� �������� (corresponding to SNAP sector 56���+"� ��������+������%����������� ���� 7 is not disaggregated into specific industries in the NERI emission database. Disaggregation into specific industries is estimated for the reporting to the Climate Convention. The disaggregation of fuel consumption and emissions from the industrial sector are discussed in a later chapter.
Both traded and non-traded fuels are included in the Danish energy sta-tistics. Thus, for example, estimation of the annual consumption of non-traded wood is included.
Petroleum coke purchased abroad and combusted in Danish residential plants (border trade of 628 TJ) is added to the apparent consumption of petroleum coke and the emissions are included in the inventory.
The DEA compiles a database for the fuel consumption of each district heating and power-producing plant based on data reported by plant op-erators. The fuel consumption of large point sources specified in the Danish emission database refers to the DEA database (DEA 2006c).
80
The fuel consumption of area sources is calculated as total fuel consump-tion minus fuel consumption of large point sources.
Emissions from non-energy use of fuels have not been included in the Danish inventory, to date, but the non-energy use of fuels is, however, included in the reference approach for Climate Convention reporting. The Danish energy statistics include three fuels used for non-energy purposes: bitumen, white spirit and lube oil. The fuels used for non-energy purposes add up to about 2% of the total fuel consumption in Denmark.
In Denmark, all municipal waste incineration is utilised for heat and power production. Thus, incineration of waste is included as stationary combustion in the IPCC Energy sector (source categories ���, ��� and ���7.
Fuel consumption data is presented in Chapter 3.2.1.
��������.�������For each fuel and SNAP category (sector and e.g. type of plant), a set of general area source emission factors has been determined. The emission factors are either nationally referenced or based on the international guidebooks EMEP/CORINAIR Guidebook (EMEP/CORINAIR, 2004) and IPCC Reference Manual (IPCC, 1997).
A complete list of emission factors, including time-series and references, is shown in Annex 3A.
.����The CO2 emission factors applied for 2005 are presented in Table 3.11. For municipal waste and natural gas, time-series have been estimated. For all other fuels the same emission factor is applied for 1990-2005.
In reporting to the Climate Convention, the CO2 emission is aggregated to five fuel types: Solid fuel, Liquid fuel, Gas, Biomass and Other fuels. The correspondence list between the NERI fuel categories and the IPCC fuel categories is also provided in Table 3.11. The emission factors are further discussed in Annex 3A.
The CO2 emission from incineration of municipal waste (94.5 + 17.6 kg/GJ) is divided into two parts: the emission from combustion of the plastic content of the waste (which is included in the national total) and the emission from combustion of the rest of the waste – the biomass part (which is reported as a memo item). In the IPCC reporting, the CO2 emission from combustion of the plastic content of the waste is reported in the fuel category, ���������# . However, this split is not applied in ei-ther fuel consumption or other emissions, as it is only relevant for CO2. Thus, the full consumption of municipal waste is included in the fuel category, 3��+� 2 and the full amount of non-CO2 emissions from mu-nicipal waste combustion is also included in the 3��+� %category.
The CO2 emission factors have been confirmed by the two major power plant operators, both directly (Christiansen, 1996 and Andersen, 1996) and indirectly, by the large power plants’ applying the NERI emission
81
factors in their annual environmental reports and by the acceptance of the NERI factors in Danish legislation.
In just adapted legislation (Law no. 493 2004), operators of large power plants are obliged to verify the applied emission factors, the input from the large power plants has not given reason to change the CO2 emission factors.
�� ���� CO2 emission factors 2005
.����The CH4 emission factors applied for 2005 are presented in Table 3.12. In general, the same emission factors have been applied for 1990-2005. However, time-series have been estimated for both natural gas fuelled engines and biogas fuelled engines. The emission factors and references are further discussed in Annex 3A.
Emission factors for gas engines, gas turbines and CHP plants combust-ing wood, straw or municipal waste all refer to emission measurements carried out on Danish plants (Nielsen & Illerup 2003). Most other emis-sion factors refer to the EMEP/CORINAIR Guidebook (EMEP/-CORINAIR, 2004).
Gas engines, combusting natural gas or biogas, contribute much more to the total CH4 emission than other stationary combustion plants. The rela-tively high emission factor for gas engines is well documented, based on a very high number of emission measurements in Danish plants. The fac-tor is further discussed in Annex 3A. Due to the considerable consump-tion of natural gas and biogas in gas engines, the IEF (implied emission factor) in CRF sector ���, ��� and ���, fuel categories 0� ��� ����# and 3��+� is relatively high. The considerable change in the IEF is a result of the increasing consumption of natural gas and biogas in gas engines as discussed in earlier.
Fuel Emission factor Unit Reference type IPCC fuel
Biomass Fossil fuel Category
Coal 95 kg/GJ Country specific Solid
Brown coal briquettes 94.6 kg/GJ IPCC reference manual Solid
FISH & RAPE OIL all all 1,5 EMEP/CORINAIR, 2004, assum-ing same emission factor as gas oil
ORIMULSION 1A1a 010101 3 EMEP/CORINAIR, 2004, assum-ing same emission factor as residual oil
NATURAL GAS 1A1a 0101, 010101, 010102, 010202 6 DGC 2001
NATURAL GAS 1A1a 010103, 010203 15 Gruijthuijsen & Jensen 2000
NATURAL GAS 1A1a, 1Ac, 1A2f, 1A4a, 1A4c
Gas turbines: 010104, 010504, 030104, 020104, 020303
1,5 Nielsen & Illerup 2003
NATURAL GAS 1A1a, 1A1c, 1A2f, 1A4a, 1A4b, 1A4c
Gas engines: 010105, 010205, 010505, 030105, 020105, 020204, 020304
1) 520 Nielsen & Illerup 2003
NATURAL GAS 1A1c, 1A2f, 1A4a, 1A4b, 1A4c
010502, 0301, 0201, 0202, 0203 6 DGC 2001
NATURAL GAS 1A2f, 1A4a, 1A4b 030103, 030106, 020103, 020202
15 Gruijthuijsen & Jensen 2000
LPG all all 1 EMEP/CORINAIR, 2004
REFINERY GAS 1A1b 010304 1,5 EMEP/CORINAIR, 2004
BIOGAS 1A1a, 1A1c, 1A2f, 1A4a, 1A4c
Gas engines: 010105, 010505, 030105, 020105, 020304
1)
323
Nielsen & Illerup 2003
BIOGAS 1A1a, 1A2f, 1A4a, 1A4c
all other 4 EMEP/CORINAIR, 2004
83
����The N2O emission factors applied for the 2005 inventory are listed in Ta-ble 3.13. The same emission factors have been applied in the period 1990-2005.
Emission factors for gas engines, gas turbines and CHP plants combust-ing wood, straw or municipal waste all refer to emission measurements carried out in Danish plants (Nielsen & Illerup 2003). Emission factor for coal-powered plants in the public power sector refers to research con-ducted by DONG Energy (Previously Elsam). Other emission factors re-fer to the EMEP/Corinair Guidebook (EMEP/CORINAIR, 2004).
84
�� ����� N2O emission factors 1990-2005
�� ���� ���*�.���"�.��Emission factors for SO2, NOX, NMVOC and CO including time-series and references are listed in Annex 3A.
The emission factors refer to:
Fuel ipcc_id SNAP_id Emission factor [g/GJ]
Reference
COAL 1A1a 0101** 0,8 Elsam 2005
COAL 1A1a, 1A1c, 1A2f, 1A4a, 1A4b, 1A4c
All except 0101** 3 EMEP/CORINAIR, 2004
BROWN COAL BRI. all all 3 EMEP/CORINAIR, 2004
COKE OVEN COKE all all 3 EMEP/CORINAIR, 2004
PETROLEUM COKE all all 3 EMEP/CORINAIR, 2004
WOOD AND SIMIL. 1A1a 010102, 010103, 010104
0,8 Nielsen & Illerup 2003
WOOD AND SIMIL. 1A1a 010105, 010202, 010203
4 EMEP/CORINAIR, 2004
WOOD AND SIMIL. 1A2f, 1A4a, 1A4b, 1A4c all 4 EMEP/CORINAIR, 2004
• Danish research reports including: • An emission measurement programme for decentralised CHP
plants (Nielsen & Illerup 2003) • Research and emission measurements programmes for biomass
fuels: • Nikolaisen et al., 1998 • Jensen & Nielsen, 1990 • Dyrnum et al., 1990 • Hansen et al., 1994 • Serup et al., 1999�
• Research and environmental data from the gas sector: • Gruijthuijsen & Jensen 2000 • Danish Gas Technology Centre 2001
• Calculations based on plant-specific emissions from a considerable number of power plants (Nielsen 2004).
• Calculations based on plant-specific emission data from a consider-able number of municipal waste incineration plants. These data refer to annual environmental reports published by plant operators.
• Sulphur-content data from oil companies and the Danish gas trans-mission company.
• Additional personal communication. Emission factor time-series have been estimated for a considerable num-ber of the emission factors. These are provided in Annex 3A.
SO2 and NOX emissions from large point sources are often plant specific based on emission measurements. Emissions of CO and NMVOC are also plant specific for some plants.
����((��(�������������.����#!����� ��!"��������The national statistics on which the emission inventories are based do not include a direct disaggregation to specific industrial subsectors. However, separate national statistics from Statistics Denmark include a disaggregation to industrial subsectors. This part of the energy statistics is also included in the official energy statistics from the Danish Energy Authority.
Every other year, Statistics Denmark collects fuel consumption data for all industrial companies of a considerable size. The deviation between the total fuel consumption from the Danish Energy Authority and the data collected by Statistics Denmark is rather small. Thus, the disaggre-gation to industrial subsectors available from Statistics Denmark can be applied for estimating disaggregation keys for fuel consumption and emissions.
Three aspects of industrial fuel consumption are considered:
• Fuel consumption for transport. This part of the fuel consumption is not disaggregated to subsectors.
86
• Fuel consumption in power or district heating plants. Disaggregation of fuel and emissions is plant specific.
• Fuel consumption for other purposes. The total fuel consumption and the total emissions are disaggregated to subsectors.
All pollutants included in the Climate Convention reporting have been disaggregated to industrial subsectors.
������ $������������#�����������������������
Time-series for fuel consumption and emissions are shown and dis-cussed in previous chapters.
Uncertainty estimates include uncertainty with regard to the total emis-sion inventory as well as uncertainty with regard to trends. The GHG emission from stationary combustion plants has been estimated with an uncertainty interval of ±8.4% and the decrease in the GHG emission since 1990 has been estimated to be -13.7% ± 2.2%-age-points.
A���#� �(��!���������(�����The Danish uncertainty estimates for GHGs are based on the Tier-1 ap-proach in IPCC Good Practice Guidance (IPCC, 2000). The uncertainty levels have been estimated for the following emission source subcatego-ries within stationary combustion:
• CO2 emission from each of the applied fuel categories • CH4 emission from gas engines • CH4 emission from all other stationary combustion plants • N2O emission from all stationary combustion plants The separate uncertainty estimation for gas engine CH4 emission and CH4 emission from other plants does not follow the recommendations in the IPCC Good Practice Guidance. Disaggregation is applied, because, in Denmark, the CH4 emission from gas engines is much larger than the emission from other stationary combustion plants and the CH4 emission factor for gas engines is estimated with a much smaller uncertainty level than for other stationary combustion plants.
Most of the applied uncertainty estimates for activity rates and emission factors are default values from the IPCC Reference Manual. A few of the uncertainty estimates are, however, based on national estimates.
87
�� ����� Uncertainty rates for activity rates and emission factors
IPCC Source category Gas Activity data uncertainty
%
Emission factor uncertainty
%
Stationary Combustion, Coal CO2 11) 53)
Stationary Combustion, BKB CO2 31) 51)
Stationary Combustion, Coke oven coke CO2 31) 51)
Stationary Combustion, Petroleum coke CO2 31) 51)
Stationary Combustion, Plastic waste CO2 54) 54)
Stationary Combustion, Residual oil CO2 21) 23)
Stationary Combustion, Gas oil CO2 41) 51)
Stationary Combustion, Kerosene CO2 41) 51)
Stationary Combustion, Orimulsion CO2 11) 23)
Stationary Combustion, Natural gas CO2 31) 13)
Stationary Combustion, LPG CO2 41) 51)
Stationary Combustion, Refinery gas CO2 31) 51)
Stationary combustion plants, gas engines CH4 2.21) 402)
Stationary combustion plants, other CH4 2.21) 1001)
Stationary combustion plants N2O 2.21) 10001)
1) IPCC Good Practice Guidance (default value)
2) Kristensen (2001)
3) Jensen & Lindroth (2002)
4) NERI assumption
����������������With regard to other pollutants, IPCC methodologies for uncertainty es-timates have been adopted for the LRTAP Convention reporting activi-ties (Pulles & Aardenne 2001). The Danish uncertainty estimates are based on the simple Tier-1 approach.
The uncertainty estimates are based on emission data and uncertainties for each of the main SNAP sectors. The assumed uncertainties for activ-ity rates and emission factors are based on default values from Pulles & Aardenne 2001. The default uncertainties for emission factors are given in letter codes representing an uncertainty range. It has been assumed that the uncertainties were in the lower end of the range for all sources and pollutants. The uncertainties for emission factors are shown in Table 3.15. The uncertainty for fuel consumption in stationary combustion plants was assumed to be 2%.
�� ����� Uncertainty rates for emission factors (%)
SNAP sector SO2 NOX NMVOC CO
01 10 20 50 20
02 20 50 50 50
03 10 20 50 20
���! ���The uncertainty estimates for stationary combustion emission invento-ries are shown in Table 3.16. Detailed calculation sheets are provided in Annex 3A.
The uncertainty interval for GHG is estimated to be ±8.4% and the uncer-tainty for the trend in GHG emission is ±2.2%-age points. The main
88
sources of uncertainty for GHG emission are the N2O emission (all plants) and the CO2 emission from coal combustion. The main source of uncertainty in the trend in GHG emission is the CO2 emission from the combustion of coal and natural gas.
The total emission uncertainty is 7% for SO2, 16% for NOX, 42% for NMVOC and 46% for CO.
�� ����� Danish uncertainty estimates, 2005
Pollutant Uncertainty
Total emission
[%]
Trend
1990-2005
[%]
Uncertainty
Trend
[%-age points]
GHG 8.4 -4.3 ± 2.2
CO2 2.6 -15 ± 1.6
CH4 42 325 ± 282
N2O 1000 9.1 ± 3.4
SO2 7 -88.4 ±0.7
NOX 16 -41 ±3
NMVOC 42 73 ±10
CO 46 49.4 ±5.3
����/� �!���������.���=7:='��#�����.�������
The elaboration of a formal QA/QC plan started in 2004. A first version is now available, Sørensen et al., 2005.
The quality manual describes the concepts of quality work and defini-tions of sufficient quality, critical control points and a list of Points for Measuring (PMs). Please see the general chapter on QA/QC.
The QC work will continue in future years.
89
�� ����� List of external data sources for stationary combustion
Data storage level 1
Since the DEA are responsible for the official Danish energy statistics as well as reporting to the IEA, NERI regards the data as being complete and in accordance with the official Danish energy statistics and IEA re-porting. The uncertainties connected with estimating fuel consumption do not, therefore, influence the accordance between IEA data, the energy statistics and the dataset on SNAP level utilised by NERI. For the re-mainder of the datasets, it is assumed that the level of uncertainty is rela-tively small. See chapter regarding uncertainties for further comments.
The uncertainty for external data is not quantified. The uncertainties of activity data and emission factors are quantified.
Dataset Description AD or Emf.
Reference Contact(s) Data agreement/ Comment
Energiproducenttællingen.xls Dataset for all electricity and heat producing plants.
Activity data
The Danish Energy Authority (DEA)
Peter Dal Data agreement in place
Gas consumption for gas engines and gas turbines 1990-1994
Activity data
DEA Peter Dal No data agreement. Historical data
Basic data (Grunddata.xls) Dataset used for IPCC reference approach
Activity data
DEA Peter Dal Not necessary. Pub-lished as part of na-tional energy statistics
Energy statistics The Danish energy statis-tics on SNAP level
Activity data
DEA Peter Dal Data agreement in place
SO2 & NOx data, plants>25 MWe Emissions DEA Marianne Niel-sen
No data agreement in place
Emission factors Emission factors stems from a large number of sources
Emission factors
See chapter regarding emis-sion factors
HM and PM from public power plants
Emissions from the two large power plant opera-tors in DK Elsam & E2
Emissions Elsam
Energi E2
Helle M. Iversen & Egon Raun Hansen.
Helle Herk-Hansen & Hen-rik Lous
No formal data agree-ment in place
Environmental reports Emissions from plants defined as large point sources
Emissions Various plants No data agreement necessary.
Plants are under obli-gation by law.
Additional data Fuel consumption and emissions from large industrial plants
AD & emissions
Aalborg Portland
Statoil
Shell
Henrik M. Thomsen
Peder Nielsen
Lis R. Rasmus-sen
No formal data agree-ment in place
Data Storage
level 1
1. Accuracy DS.1.1.1 General level of uncertainty for every dataset including the reasoning for the specific values
Data Storage
level 1
1. Accuracy DS.1.1.2 Quantification of the uncertainty level of every single data value, including the reasoning behind the specific values.
90
On the external data, the comparability has not been checked. However, at CRF level, a project has been carried out comparing the Danish inven-tories with those of other countries.
See the above table for an overview of external datasets.
��������(��7!������� ����������������������������������"�������������(���"���/���������This statistics takes the form of a spreadsheet from the DEA listing fuel consumption of all plants included as large point sources in the emission inventory. The statistics on fuel consumption from district heating and power plants are regarded as complete and with no significant uncer-tainty since the plants are bound by law to report their fuel consumption and other information.
!������������������(�����(�������"�(�����,�����6BBC�6BB@��For the years 1990-1994, the DEA has estimated consumption of natural gas and biogas in gas engines and gas turbines. NERI assesses that the estimation by the DEA is the best available data.
������"����These data takes the form of a spreadsheet from DEA used for the CO2 emission calculation in accordance with the IPCC reference approach. It is published annually on the DEA’s webpage; therefore, a formal data delivery agreement is not deemed necessary.
���(���������������� �+%���1����The DEA reports fuel consumption statistics on the SNAP level based on a correspondence table developed in co-operation with NERI. Both traded and non-traded fuels are included in the Danish energy statistics. Thus, for example, estimation of the annual consumption of non-traded wood is included. Petroleum coke, purchased abroad and combusted in Danish residential plants (border trade), is added to the apparent con-sumption of petroleum coke and the emissions are included in the inven-tory.
Emissions from non-energy use of fuels have not been included in the Danish inventory, to date, but the non-energy use of fuels is, however, included in the reference approach for Climate Convention reporting.
�����"��������������"���������������������"����(��������D�8<�$��Plants larger than 25 MWe are obligated to report SO2 and NOx emission data to the DEA annually. Data are on block level and are classified. The data on plant level are part of the plants’ annual environmental reports. NERI’s QC of the data consists of a comparison with data from previous years and with data from the plants’ annual environmental reports.
Data Storage
level 1
2.Comparability DS.1.2.1 Comparability of the data values with similar data from other countries, which are comparable with Denmark, and evaluation of discrepancy.
Data Storage
level 1
3.Completeness DS.1.3.1 Documentation showing that all pos-sible national data sources are in-cluded, by setting down the reasoning behind the selection of datasets
91
����������������������/�"����(������������For specific references, see chapter regarding emission factors.
��"����(���8�The two major Danish power plant operators assess heavy metal emis-sions from their plants using model calculations based on fuel data and type of flue-gas cleaning. NERI’s QC of the data consists of a comparison with data from previous years and with data from the plants’ annual en-vironmental reports.
+��������1���������������������������"�����"������(���������������A large number of plants are obligated by law to publish an environ-mental report annually with information on emissions, among other things. NERI compares data with those from previous years and large discrepancies are checked.
�����������(�"����������(����"����������,��������������Fuel consumption and emission data from a few large industrial com-bustion plants are obtained directly from the plants concerned. NERI compares data with those from previous years and large discrepancies are checked.
It is ensured that all external data are archived at NERI. Subsequent data processing takes place in other spreadsheets or databases. The datasets are archived annually in order to ensure that the basic data for a given report are always available in their original form.
For stationary combustion, a data delivery agreement is made with the DEA. Most of the other external data sources are available due to legisla-tive requirements. See Table 3.17.
See DS 1.3.1
See Table 3.17 for general references. Much documentation already ex-ists. However, some of the information used is classified and, therefore, not publicly available.
Data Storage
level 1
4.Consistency DS.1.4.1 The origin of external data has to be pre-served whenever possible without explicit arguments (referring to other PMs)
Data Storage
level 1
6.Robustness DS.1.6.1 Explicit agreements between the external institution holding the data and NERI about the condition of delivery
Data Storage
level 1
7.Transparency DS.1.7.1 Summary of each dataset, including the reasoning behind selection of the specific dataset
Data Storage
level 1
7.Transparency DS.1.7.3 References for citation for any external dataset have to be available for any single number in any dataset.
Data Storage
level 1
7.Transparency DS.1.7.4 Listing of external contacts for every data-set
92
See Table 3.17
1����$���� ����8�)�#���
The uncertainty assessment of activity data and emission factors is dis-cussed in the chapter concerning uncertainties.
The uncertainty assessment of activity data and emission factors is dis-cussed in the chapter concerning uncertainties.
The methodological approach is consistent with international guidelines.
Calculated emission factors are compared with guideline emission fac-tors to ensure that they are reasonable.
The calculations follow the principle in international guidelines.
Regarding the distribution of energy consumption for industrial sources, a more detailed and frequently updated data material would be pre-ferred. There is ongoing work to increase the accuracy and completeness of this sector. It is not assessed that this has any influence on the esti-mates for the emission of greenhouse gases.
There is no problem with regard to access to critical data sources.
Data Processing
level 1
1. Accuracy DP.1.1.1 Uncertainty assessment for every data source as input to Data Storage level 2 in relation to type of variability (Distribution as: normal, log normal or other type of variabil-ity)
Data Processing
level 1
1. Accuracy DP.1.1.2 Uncertainty assessment for every data source as input to Data Storage level 2 in relation to scale of variability (size of varia-tion intervals)
Data Processing
level 1
1. Accuracy DP.1.1.3 Evaluation of the methodological approach using international guidelines
Data Processing
level 1
1. Accuracy DP.1.1.4 Verification of calculation results using guideline values
Data Processing
level 1
2.Comparability DP.1.2.1 The inventory calculation has to follow the international guidelines suggested by UNFCCC and IPCC.
Data Processing
level 1
3.Completeness DP.1.3.1 Assessment of the most important quanti-tative knowledge which is lacking.
Data Processing
level 1
3.Completeness DP.1.3.2 Assessment of the most important cases where access is lacking with regard to critical data sources that could improve quantitative knowledge.
93
A change in calculating procedure would entail that an updated descrip-tion would be elaborated.
During data processing, it is checked that calculations are being carried out correctly, however, a documentation system for this needs to be elaborated.
A time-series for activity data on SNAP level, as well as emission factors, is used to identify possible errors in the calculation procedure.
The IPCC reference approach validates the fuel consumption rates and CO2 emissions of fuel combustion. Fuel consumption rates and CO2 emissions differ by less than 1.6% (1990-2005). The reference approach is further discussed below.
There is a direct line between the external datasets, the calculation proc-ess and the input data used to Data storage level 2. During the calcula-tion process, numerous controls are in place to ensure correctness, e.g. sum checks of the various stages in the calculation procedure.
Where appropriate this is included in the present report with annexes.
There is a clear line between external data and the data processing.
Data Processing
level 1
4.Consistency DP.1.4.1 In order to keep consistency at a high level,, an explicit description of the activi-ties needs to accompany any change in the calculation procedure
Data Processing
level 1
5.Correctness DP.1.5.1 Demonstration at least once, by independent calculation, the correctness of every data ma-nipulation
Data Processing
level 1
5.Correctness DP.1.5.2 Verification of calculation results using time-series
Data Processing
level 1
5.Correctness DP.1.5.3 Verification of calculation results using other measures
Data Processing
level 1
5.Correctness DP.1.5.4 Shows one-to-one correctness be-tween external data sources and the databases at Data Storage level 2
Data Processing
level 1
7.Transparency DP.1.7.1 The calculation principle and equa-tions used must be described.
Data Processing
level 1
7.Transparency DP.1.7.2 The theoretical reasoning for all methods must be described.
Data Processing
level 1
7.Transparency DP.1.7.3 Explicit listing of assumptions behind all methods.
Data Processing
level 1
7.Transparency DP.1.7.4 Clear reference to dataset at Data Storage level 1
94
At present a manual log table is not in place on this level, however this feature will be implemented in the future. A manual log table is incorpo-rated in the national emission database, Data Storage level 2.
To ensure a correct connection between data on level 2 to data on level 1, different controls are in place, e.g. control of sums and random tests.
Data import is checked by use of sum control and random testing. The same procedure is applied every year in order to minimise the risk of data import errors.
6����='������#!����The emission from each large point source is compared with the emis-sion reported the previous year.
Some automated checks have been prepared for the emission databases:
• Checking units for fuel rate, emission factor and plant-specific emis-sions
• Checking emission factors for large point sources. Emission factors for pollutants that are not plant-specific should be the same as those de-fined for area sources.
• Additional checks on database consistency • Most emission factor references are now incorporated in the emission
database, itself. • Annual environmental reports are kept for subsequent control of
plant-specific emission data. • QC checks of the country-specific emission factors have not been per-
formed, but most factors are based on work from companies that have implemented some QA/QC work. The two major power plant own-ers / operators in Denmark, E2 and Elsam, both obtained the ISO 14001 certification for an environmental management system. Danish Gas Technology Centre and Force both run accredited laboratories for emission measurements.
!((����#�=7:='�� ��.����������������"!�����The following points make up the list of QA/QC tasks to be carried out directly in relation to the stationary combustion part of the Danish emis-sion inventories. The time plan for the individual tasks has not yet been made.
2��������(����1���6�
• A fully comprehensive list of references for emission factors and ac-tivity data.
Data Processing
level 1
7.Transparency DP.1.7.5 A manual log to collect information about recalculations
Data Storage level 2
5.Correctness DS.2.5.1 Documentation of a correct connection between all data types at level 2 to data at level 1
Data Storage level 2
5.Correctness DS.2.5.2 Check if a correct data import to level 2 has been made.
95
• A comparison with external data from other countries in order to evaluate discrepancies.
2������������(���1���6�
• Documentation list of model and independent calculations to test every single mathematical relation.
����,� �!���������.������� �! ������
Improvements and recalculations since the 2007 emission inventory in-clude:
• Update of fuel rates according to the latest energy statistics. The up-date includes the years 1980-2004.
• The emission factor for NMVOC for residential wood combustion has been updated for 1980-2004 because of new research carried out by NERI.
• New data material has made it possible to update the disaggregation of sector 1A2 into subsectors. This has not influenced the total emis-sion from sector 1A2 only the distribution on sectors 1A2a-1A2f.
������ �!���������.���� ��#�������������
Some planned improvements to the emission inventories are discussed below.
*2��������#�#��!��������.�����������.�������The reporting of, and references for, the applied emission factors have been improved in the current year and will be further developed in fu-ture inventories.
�2�=7:='��#��� �#�����The work with implementing and expanding the QA/QC procedures will continue in future years
�2�$�������������������Uncertainty estimates are largely based on default uncertainty levels for activity rates and emission factors. More country-specific uncertainty es-timates will be incorporated in future inventories.
The emission inventory basis for mobile sources is fuel use information from the Danish energy statistics. In addition, background data for road transport (fleet and mileage), air traffic (aircraft type, flight numbers, origin and destination airports) and non-road machinery (engine no., engine size, load factor and annual working hours) are used to make the emission estimates sufficiently detailed. Emission data mainly comes from the EMEP/CORINAIR Emission Inventory Guidebook. However, for railways, specific Danish measurements are used.
In the Danish emissions database, all activity rates and emissions are de-fined in SNAP sector categories (Selected Nomenclature for Air Pollu-tion) according to the CORINAIR system. The emission inventories are prepared from a complete emission database based on the SNAP sectors.
96
The aggregation to the sector codes used for both the UNFCCC and UN-ECE Conventions is based on a correspondence list between SNAP and IPCC classification codes (CRF), shown in the table below (mobile sources only).
SNAP classification IPCC classification
07 Road transport 1A3b Transport-Road
0801 Military 1A5 Other
0802 Railways 1A3c Railways
0803 Inland waterways 1A3d Transport-Navigation
080402 National sea traffic 1A3d Transport-Navigation
080403 National fishing 1A4c Agriculture/forestry/fisheries
080404 International sea traffic 1A3d Transport-Navigation (international)
�� ����� SNAP – CRF correspondence table for transport
Military transport activities (land and air) refer to the CRF sector Other (1A5), while the Transport-Navigation sector (1A3d) comprises national sea transport (ship movements between two Danish ports) and small boats and pleasure crafts. The working machinery and materiel in indus-try is grouped in Industry-Other (1A2f), while agricultural and forestry machinery is accounted for in the Agriculture/forestry/fisheries (1A4c) sector together with fishing activities.
����*� �!��������(����#����������
The following description of source categories explains the development in fuel consumption and emissions for road transport and other mobile sources.
%!� ����!������
�� ����� Fuel use (PJ) for domestic transport in 2005 in CRF sectors
CRF ID Fuel use (PJ)
Industry-Other (1A2f) 13.0
Civil Aviation (1A3a) 1.9
Road (1A3b) 165.3
Railways (1A3c) 3.1
Navigation (1A3d) 7.3
Residential (1A4b) 4.1
Ag./for./fish. (1A4c) 21.3
Military (1A5) 3.7
Total 219.6
97
Table 3.19 shows the fuel use for domestic transport based on DEA sta-tistics for 2005 in CRF sectors. The fuel use figures in time-series 1990-2005 are given in Annex 3.B.14 (CRF format) and are shown for 1990 and 2005 in Annex 3.B.13 (CollectER format). Road transport has a major share of the fuel consumption for domestic transport. In 2005 this sec-tor’s fuel use share is 75%, while the fuel use shares for Agricul-ture/forestry/fisheries and Industry-Other are 10 and 6%, respectively. For the remaining sectors the total fuel use share is 9%.
From 1985 to 2005, diesel and gasoline fuel use has increased by 39% and 25%, respectively, and in 2005 the fuel use shares for diesel and gasoline were 60% and 37%, respectively (Figures 3.15 and 3.16). Other fuels only have a 3% share of the domestic transport total. Almost all gasoline is used in road transportation vehicles. Gardening machinery and recrea-tional craft are merely small consumers. Regarding diesel, there is con-siderable fuel use in most of the domestic transport categories, whereas a more limited use of residual oil and jet fuel is being used in the naviga-tion sector and by aviation (civil and military flights), respectively.
����#����������As shown in Figure 3.17, the energy use for road transport increased un-til 2000, where a small fuel use decline can be noted. From 2002 onwards, fuel consumption increases. The fuel use development is due to a slight decrease in the use of gasoline fuels from 1999 onwards combined with a steady growth in the use of diesel. Within subsectors, passenger cars rep-resent the most fuel-consuming vehicle category, followed by heavy-duty vehicles, light duty vehicles and 2-wheelers, in decreasing order (Figure 3.18).
0
20
40
60
80
100
120
140
1985
1987
1989
1991
1993
1995
1997
1999
2001
2003
2005
���� Diesel
GasolineOther
Diesel60%
Gasoline37%
Kerosene0%
Jet fuel2%
LPG0%
AvGas0%
Residual oil1%
��������� Fuel consumption per fuel type for do-mestic transport 1985-2005
��������� Fuel use share per fuel type for domestic transport in 2005
98
As shown in Figure 3.19, fuel consumption for gasoline passenger cars dominates the overall gasoline consumption trend. The development in diesel fuel consumption in recent years (Figure 3.20) is characterised by increasing fuel use for diesel passenger cars and light duty vehicles, while the fuel use for trucks and buses (heavy-duty vehicles), since 1999, has fluctuated. The sudden increase in fuel consumption for heavy-duty vehicles in 2003 is, however, significant.
0
20
40
60
80
100
120
140
160
180
1985
1987
1989
1991
1993
1995
1997
1999
2001
2003
D;EF
DieselGasoline
Total
��������� Fuel consumption per fuel type and as totals for road transport 1985-2005
0102030405060708090
10019
85
1987
1989
1991
1993
1995
1997
1999
2001
2003
2005
D;EF
2-wheelers
Heavy duty vehicles
Light duty vehicles
Passenger cars
��������� Total fuel consumption per vehicle type for road transport 1985-2005
010
20
30
4050
6070
8090
1985
1987
1989
1991
1993
1995
1997
1999
2001
2003
2005
D;EF
2-wheelers
Heavy duty vehicles
Light duty vehicles
Passenger cars
��������� Gasoline fuel consumption per vehicle type for road transport 1985-2005
99
In 2005, fuel consumption shares for gasoline passenger cars, heavy-duty vehicles, diesel light duty vehicles, diesel passenger cars and gasoline light duty vehicles were 44, 26, 17, 10 and 2%, respectively (Figure 3.21).
�6������"� ����!�����It must be noted that the fuel use figures behind the Danish inventory for mobile equipment in the agriculture, forestry, industry, household and gardening (residential), and inland waterways (part of navigation) sec-tors, are less certain than for other mobile sectors. For these types of ma-chinery, the DEA statistical figures do not directly provide fuel use in-formation, and fuel use totals are subsequently estimated from activity data and fuel use factors. For 2005 no new stock information has been gathered for the machinery types in household and gardening and for recreational craft, and thus the 2004 total stock information is repeated for this year.
As seen in Figure 3.22, classified according to CRF the most important sectors are Agriculture/forestry/fisheries (1A4c), Industry-other (mobile machinery part of 1A2f) and Navigation (1A3d). Minor fuel consuming sectors are Civil Aviation (1A3a), Railways (1A3c), Other (military mo-bile fuel use: 1A5) and Residential (1A4b).
The 1985-2005 time-series are shown per fuel type in Figures 3.23-3.26 for diesel, gasoline and jet fuel, respectively.
05
101520253035404550
1985
1987
1989
1991
1993
1995
1997
1999
2001
2003
2005
D;EF
Heavy duty vehicles
Light duty vehicles
Passenger cars
���������� Diesel fuel consumption per vehicle type for road transport 1985-2005
LPG PC0%
Diesel LDV17%
Gasoline PC44%
Diesel HDV26%
Gasoline LDV2%
w heelers-21%
Gasoline HDV0%
Diesel PC10%
��������� Fuel use share (PJ) per vehicle type for road transport in 2005
100
0
1
2
3
4
5
1985
1987
1989
1991
1993
1995
1997
1999
2001
2003
2005
����
Navigation (1A3d)
���������� Residual oil fuel use in CRF sectors for other mobile sources 1985-2005
0
5
10
15
20
25
30
1985
1987
1989
1991
1993
1995
1997
1999
2001
2003
2005
����
Military (1A5)
Railways (1A3c)
Navigation (1A3d)
Ag./for./fish. (1A4c)
Civil Aviation (1A3a)
Industry-Other (1A2f)
Residential (1A4b)
���������� Total fuel use in CRF sectors for other mobile sources 1985-2005
02468
1012141618
1985
1987
1989
1991
1993
1995
1997
1999
2001
2003
2005
D;EF
Military (1A5)
Railways (1A3c)
Navigation (1A3d)
Agr./for. (1A4c)
Industry-Other (1A2f)
Fisheries (1A4c)
���������� Diesel fuel use in CRF sectors for other mobile sources 1985-2005
0,00,51,01,52,02,53,03,54,04,5
1985
1987
1989
1991
1993
1995
1997
1999
2001
2003
2005
D;EF
Military (1A5)
Railways (1A3c)
Navigation (1A3d)
Agr./for. (1A4c)
Industry-Other (1A2f)
Residential (1A4b)
���������� Gasoline fuel use in CRF sectors for other mobile sources 1985-2005
101
In terms of diesel, the fuel use decreases for agricultural machines until 2000, due to fewer numbers of tractors and harvesters. After that, the in-crease in the engine sizes of new sold machines has more than outbal-anced the trend towards smaller total stock numbers. The fuel use for in-dustry has increased from the beginning of the 1990’s, due to an increase in the activities for construction machinery. For fisheries, the develop-ment in fuel use reflects the activities in this sector.
The Navigation sector comprises national sea transport (fuel use be-tween two Danish ports) and recreational craft. For the latter category, fuel use has increased significantly from 1985 to 2004 due to the rising number diesel-fuelled private boats. For national sea transport, diesel fuel use shows some fluctuations over the same time period. However, for 1997 and 1998, a sudden decline in fuel use is apparent. The most im-portant explanation here is the closing of ferry service routes in connec-tion with the opening of the Great Belt Bridge in 1997. For railways, the gradual shift towards electrification explains the lowering trend in diesel fuel use and the emissions for this transport sector. The fuel used (and associated emissions) to produce electricity is accounted for in the sta-tionary source part of the Danish inventories.
The largest gasoline fuel use is found for household and gardening ma-chinery in the Residential (1A4b) sector. Especially from 2001-2004, a significant fuel use increase is apparent due to considerable growth in the machinery stock. The decline in gasoline fuel use for Agricul-ture/forestry/fisheries (1A4c) is due to the gradual phasing out of gaso-line-fuelled agricultural tractors.
The considerable variations from one year to another in military jet fuel use are due to planning and budgetary reasons, and the passing demand for flying activities. Consequently, for some years, a certain amount of jet fuel stock-building might disturb the real picture of aircraft fuel use. Civil aviation has decreased since the building of the Great Belt Bridge, both in terms of number of flights and total jet fuel use.
In terms of residual oil there has been a substantial decrease in the fuel use for sea vessels. The fuel use decline is most significant from 1991-1997.
<!�����The residual oil and diesel oil fuel use fluctuations reflect the quantity of fuel sold in Denmark to international ferries, international warships, other ships with foreign destinations, transport to Greenland and the Faroe Islands, tank vessels and foreign fishing boats. For jet petrol, the
0
1
2
3
4
5
1985
1987
1989
1991
1993
1995
1997
1999
2001
2003
2005
����
Military (1A5)
Civil Aviation (1A3a)
���������� Jet fuel use in CRF sectors for other mobile sources 1985-2005
102
sudden fuel use drop in 2002 is explained by the recession in the air traf-fic sector due to the events of September 11, 2001 and structural changes in the aviation business.
�����������.�'6�)�'B���#���6�In Table 3.20 the CO2, CH4 and N2O emissions for road transport and other mobile sources are shown for 2005 in CRF sectors. The emission figures in time-series 1985-2005 are given in Annex 3:B.13 (CRF format) and are shown for 1990 and 2005 in Annex 3.B.13 (CollectER format).
From 1985 to 2005 the road transport emissions of CO2 and N2O have in-creased by 49 and 320%, respectively, whereas the emissions of CH4 have decreased by 12%. From 1990-2005 the CO2 and N2O emission increases are 31 and 251%, respectively, whereas for CH4 the emissions decrease by 17% (from Figures 3.28-3.30). From 1985 and 1990, to 2005 the other mobile CO2 emissions have decreased by 10 and 6%, respectively (from Figures 3.32-3.34).
�� ������ Emissions of CO2, CH4 and N2O in 2005 for road transport and other mobile sources
CRF Sector CH4 CO2 N2O
[tons] [ktons] [tons]
Industry-Other (1A2f) 45 950 40
Civil Aviation (1A3a) 7 133 8
Railways (1A3c) 9 232 6
Navigation (1A3d) 35 543 31
Residential (1A4b) 291 297 5
Ag./for./fish. (1A4c) 74 1573 76
Military (1A5) 15 271 13
Total other mobile 476 3999 179
Road (1A3b) 2280 12157 1384
Total mobile 45 950 40
���#����������CO2 emissions are directly fuel-use dependent and, in this way, the de-velopment in the emission reflects the trend in fuel use. As shown in Figure 3.28, the most important emission source for road transport is passenger cars, followed by heavy-duty vehicles, light-duty vehicles and 2-wheelers in decreasing order. In 2005, the respective emission shares were 53, 27, 19 and 1%, respectively (Figure 3.31).
0
5
10
15
20
25
30
35
40
45
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
����
Jet fuelDieselResidual oil
���������� Bunker fuel use 1985-2005
103
The majority of CH4 emissions from road transport come from gasoline passenger cars (Figure 3.29). The emission increase from 1990 to 1996 for this vehicle category is a result of the somewhat higher emission factors for EURO I gasoline cars (introduced in 1990) than for conventional gasoline cars. The emission drop from 1997 onwards is explained by the penetration of EURO II and III catalyst cars (1997 and 2001) into the Dan-ish fleet. The newer technology stages have lower CH4 emission factors than conventional gasoline vehicles. The 2005 emission shares for CH4 were 68, 18, 8 and 6% for passenger cars, heavy-duty vehicles, 2-wheelers and light-duty vehicles, respectively (Figure 3.31).
An undesirable environmental side effect of the introduction of catalyst cars is the increase in the emissions of N2O from 1990 onwards (Figure 3.30). However, the total contributions from road transport N2O and CH4 emissions are still small compared with the contribution from the agri-cultural sector. In 2005, emission shares for passenger cars, light and heavy-duty vehicles were 76, 14 and 10%, of the total road transport N2O, respectively (Figure 3.31).
0
1000
2000
3000
4000
5000
6000
7000
1985
1987
1989
1991
1993
1995
1997
1999
2001
2003
2005
D����F
2-wheelers Heavy duty vehicles Light duty vehicles Passenger cars
���������� CO2 emissions (k-tonnes) per vehicle type for road transport 1985-2005
0
500
1000
1500
2000
2500
3000
1985
1987
1989
1991
1993
1995
1997
1999
2001
2003
2005
D���F
2-wheelers Heavy duty vehicles Light duty vehicles Passenger cars
���������� CH4 emissions (tonnes) per vehicle type for road transport 1985-2005
104
Referring to the third IPCC assessment report, 1 g CH4 and 1 g N2O has the greenhouse effect of 21 and 310 g CO2, respectively. In spite of the relatively large CH4 and N2O global warming potentials, the largest con-tribution to the total CO2 emission equivalents for road transport comes from CO2, and the CO2 emission equivalent shares per vehicle category are almost the same as the CO2 shares.
���
Heavy duty vehicles
27%
2-wheelers1%
Passenger cars53%
Light duty vehicles
19%
���
Light duty vehicles
6%
Passenger cars68%
2-wheelers8%
Heavy duty vehicles
18%
���
Light duty vehicles
14%
Passenger cars76%
2-wheelers0%
Heavy duty vehicles
10%
�������������
Light duty vehicles
19%
Passenger cars54%
2-wheelers1%
Heavy duty vehicles
26%
��������� CO2, CH4 and N2O emission shares and GHG equiva-lent emission distribution for road transport in 2005
�6������"� ����!�����For other mobile sources, the highest CO2 emissions in 2005 come from Agriculture/forestry/fisheries (1A4c), Industry-other (1A2f), Navigation (1A3d), with shares of 39, 24 and 14%, respectively (Figure 3.35). The 1985-2005 emission trend is directly related to the fuel-use development in the same time-period. Minor CO2 emission contributors are sectors such as Residential (1A4b), Railways (1A3c), Military (1A5) and Civil Aviation (1A3a). In 2005, the CO2 emission shares for these sectors were 7, 6, 7 and 3%, respectively (Figure 3.35).
0
200
400
600
800
1000
1200
1985
1987
1989
1991
1993
1995
1997
1999
2001
2003
2005
D���F
2-wheelers Heavy duty vehicles Light duty vehicles Passenger cars
���������� N2O emissions (tonnes) per vehicle type for road transport 1985-2005
105
For CH4, far the most important sector is Residential (1A4b), see Figure 3.35. The emission share of 62% in 2005 is due to a relatively large gaso-line fuel use for gardening machinery. The 2005 emission shares for Ag-riculture/forestry/fisheries (1A4c), Industry (1A2f) and Navigation (1A3d) are 16, 9 and 7%, respectively, whereas the remaining sectors have emission shares of 3% or less.
For N2O, the emission trend in subsectors is the same as for fuel use and CO2 emissions (Figure 3.34).
As for road transport, CO2 alone contributes with by far the most CO2 emission equivalents in the case of other mobile sources, and the sectoral CO2 emission equivalent shares are almost the same as those for CO2, it-self (Figure 3.35).
���������� CH4 emissions (tonnes) in CRF sectors for other mobile sources 1985-2005
106
����������.� 6�)��6�)��AC6'��#�'6�In Table 3.21 the SO2, NOX, NMVOC and CO emissions for road trans-port and other mobile sources are shown for 2005 in CRF sectors. The emission figures in the time-series 1985-2005 are given in Annex 3.B.14 (CRF format) and are shown for 1990 and 2005 in Annex 3.B.13 (Collec-tER format).
From 1985 to 2005, the road transport emissions of NMVOC, CO and NOX emissions have decreased by 70, 66 and 26%, respectively (Figures 3.37-3.39). The highest CO, NOX and NMVOC emissions occur in 1991, after which the emissions drop by 60, 36 and 70%, respectively, until 2005.
For other mobile sources, the emissions of NOX decreased by 3% from 1985 to 2005 and for SO2 the emission drop is as much as 79%. In the
���������� N2O emissions (tonnes) in CRF sectors for other mobile sources 1985-2005
���
Residential (1A4b)
7%
Fisheries (1A4c)12%
Navigation (1A3d)14%
Industry-Other (1A2f)
24%
Agr./for. (1A4c)27%
Civil Aviation (1A3a)
3%Military (1A5)7%
Railways (1A3c)
6%
���
Residential (1A4b)62%
Fisheries (1A4c)
2%
Navigation (1A3d)
7%
Industry-Other (1A2f)
9%
Agr./for. (1A4c)14%
Civil Aviation (1A3a)
1%
Military (1A5)3%
Railways (1A3c)
2%
���
Residential (1A4b)
3%
Fisheries (1A4c)17%
Navigation (1A3d)17%
Industry-Other (1A2f)
22%
Agr./for. (1A4c)26%
Civil Aviation (1A3a)
4%
Military (1A5)7%
Railways (1A3c)
4%
�������������
Railways (1A3c)
6%
Military (1A5)7%
Civil Aviation (1A3a)
4%
Agr./for. (1A4c)29%
Industry-Other (1A2f)
26%
Navigation (1A3d)15%
Fisheries (1A4c)13%
���������� CO2, CH4 and N2O emission shares and GHG equivalent emission distribution for other mobile sources in 2005
107
same period, the emissions of NMVOC have declined by 8%, whereas the CO emissions have increased by 1% (Figures 3.41-3.44).
�� ����� Emissions of SO2, NOX, NMVOC and CO in 2005 for road transport and other mobile sources
CRF ID SO2 NOX NMVOC CO
[tons] [tons] [tons] [tons]
Industry-Other (1A2f) 28 10664 1620 7497
Civil Aviation (1A3a) 43 579 165 858
Railways (1A3c) 1 3724 235 648
Navigation (1A3d) 1975 9645 1394 7802
Residential (1A4b) 2 327 8727 115088
Ag./for./fish. (1A4c) 628 20713 2483 15899
Military (1A5) 57 1332 113 821
Total other mobile 2733 46985 14737 148612
Road (1A3b) 77 68105 24325 188071
Total mobile 28 10664 1620 7497
���#����������The step-wise lowering of the sulphur content in diesel fuel has given rise to a substantial decrease in the road transport emissions of SO2 (Fig-ure 3.36). In 1999, the sulphur content was reduced from 500 ppm to 50 ppm (reaching gasoline levels), and for both gasoline and diesel the sul-phur content was reduced to 10 ppm in 2005. Since Danish diesel and gasoline fuels have the same sulphur percentages, at present, the 2005 shares for SO2 emissions and fuel use for passenger cars, heavy-duty ve-hicles, light-duty vehicles and 2-wheelers are the same in each case: 53, 27, 19 and 1%, respectively (Figure 3.40).
Historically, the emission totals of NMVOC and CO have been very dominated by the contributions coming from private cars, as shown in Figures 3.38-3.39. However, the NMVOC and CO (and NOx) emissions from this vehicle type have shown a steady decreasing tendency since the introduction of private catalyst cars in 1990 (EURO I) and the intro-duction of even more emission-efficient EURO II and III private cars (in-troduced in 1997 and 2001, respectively).
0
1000
2000
3000
4000
5000
6000
7000
8000
1985
1987
1989
1991
1993
1995
1997
1999
2001
2003
2005
D���F
2-wheelers Heavy duty vehicles Light duty vehicles Passenger cars
���������� SO2 emissions (tonnes) per vehicle type for road transport 1985-2005
108
In the case of NOx, the real traffic emissions for heavy duty vehicles do not decline follow the reductions as intended by the EU emission legisla-tion. This is due to the so-called engine cycle-beating effect. Outside the legislative test cycle stationary measurement points, the electronic engine control for heavy duty Euro II and III engines switches to a fuel efficient engine running mode, thus leading to increasing NOx emissions.
In 2005, the emission shares for heavy-duty vehicles, passenger cars, light-duty vehicles and 2-wheelers were 52, 33, 15 and 0%, respectively, for NOX; 6, 67, 7 and 20%, respectively, for NMVOC; and 4, 81, 7 and 8%, respectively, for CO (Figure 3.40).
0
10000
20000
30000
40000
50000
60000
70000
1985
1987
1989
1991
1993
1995
1997
1999
2001
2003
2005
D���F
2-wheelers Heavy duty vehicles Light duty vehicles Passenger cars
���������� NOX emissions (tonnes) per vehicle type for road transport 1985-2005
0
10000
20000
30000
40000
50000
60000
70000
80000
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
Passenger Cars Light Duty Vehicles Heavy Duty Vehicles 2-wheelers
���������� NMVOC emissions (tonnes) per vehicle type for road transport 1985-2005
109
�6������"� ����!�����SO2 emissions decrease significantly from 1985 to 1996, as shown in Fig-ure 3.41. The lowering is due to a decrease in the use of residual oil by ships in Navigation (1A3d) and a reduction in sulphur content for ma-rine gas oil in Navigation (1A3d) and Fisheries (1A4c), and sulphur con-tent reductions for diesel used by, among others, Railways (1A3c) and non-road machinery in Agriculture/forestry (1A4c) and Industry (1A2f).
0
100000
200000
300000
400000
500000
600000
1985
1987
1989
1991
1993
1995
1997
1999
2001
2003
2005
������
2-wheelers Heavy duty vehicles Light duty vehicles Passenger cars
���������� CO emissions (tonnes) per vehicle type for road transport 1985-2005
���
Heavy duty vehicles
27%
2-wheelers1%
Passenger cars53%
Light duty vehicles
19%
���
Light duty vehicles
15%
Passenger cars33%
2-wheelers0%
Heavy duty vehicles
52%
�����
Passenger Cars67%
Light Duty Vehicles
7%
Heavy Duty Vehicles
6%
2-wheelers20%
��
Light duty vehicles
7%
Passenger cars81%
2-wheelers8% Heavy duty
vehicles4%
���������� SO2, NOX, NMVOC and CO emission shares per vehicle type for road transport in 2005
110
In general, the emissions of NOX, NMVOC and CO from diesel-fuelled working equipment and machinery in agriculture, forestry and industry have decreased slightly since the end of the 1990s due to gradually strengthened emission standards given by the EU emission legislation directives.
NOX emissions mainly come from diesel machinery, and the most impor-tant sources are Agriculture/forestry/fisheries (1A4c), Industry (1A2f), Navigation (1A3d) and Railways (1A3c), as shown in Figure 3.42. The 2005 emission shares are 43, 23, 21 and 8%, respectively (Figure 3.45). Minor emissions come from the sectors, Civil Aviation (1A3a), Military (1A5) and Residential (1A4b).
The NOX emission trend for Navigation, Fisheries and Agriculture is de-termined by fuel use fluctuations for these sectors, and the development of emission factors. For ship engines the emission factors tend to increase for new engines until mid 1990’s. After that, the emission factors gradu-ally reduce until 2000, bringing them to a level comparable with the emission limits for new engines in this year. For agricultural machines, there have been somewhat higher NOx emission factors for 1991-stage I machinery, and an improved emission performance for stage I and II machinery since the late 1990s.
The emission development for industry NOx is the product of a slight fuel-use increase from 1985 to 2005 and a development in emission fac-tors as explained for agricultural machinery. For railways, the gradual shift towards electrification explains the declining trend in diesel fuel use and NOX emissions for this transport sector until 2001.
���������� NOX emissions (tonnes) in CRF sectors for other mobile sources 1985-2005
111
The 1985-2005 time-series of NMVOC and CO emissions are shown in Figures 3.43 and 3.44 for other mobile sources. The 2005 sector emission shares are shown in Figure 3.45. For NMVOC, the most important sec-tors are Residential (1A4b), Agriculture/forestry/fisheries (1A4c), Indus-try (1A2f) and Navigation (1A3d), with 2005 emission shares of 59, 17, 11 and 9%, respectively. The same four sectors also contribute with most of the CO emissions in the same consecutive order; the emission shares are 77, 11, 5 and 5%, respectively. Minor NMVOC and CO emissions come from Railways (1A3c), Civil Aviation (1A3a) and Military (1A5).
For NMVOC and CO, the significant emission increases for the residen-tial sector after 2000 are due to the increased number of gasoline working machines. Improved NMVOC emission factors for diesel machinery in agriculture and gasoline equipment in forestry (chain saws) are the most important explanations for the NMVOC emission decline in the Agricul-ture/forestry/fisheries sector. This explanation also applies for the in-dustrial sector, which is dominated by diesel-fuelled machinery. From 1997 onwards, the NMVOC emissions from Navigation decrease due to the gradually phase-out of the 2-stroke engine technology for recrea-tional craft. The main reason for the significant 1985-2005 CO emission decrease for Agriculture/forestry/fisheries is the phasing out of gasoline tractors.
���������� CO emissions (tonnes) in CRF sectors for other mobile sources 1985-2005
112
�<!�����The most important emissions from bunker fuel use (fuel use for interna-tional transport) are SO2, NOX and CO2 (and TSP, not shown). However, compared with the Danish national emission total (all sources), the greenhouse gas emissions from bunkers are small. The bunker emission totals are shown in Table 3.22 for 2005, split into sea transport and civil aviation. All emission figures in the 1985-2005 time-series are given in Annex 3.B.14 (CRF format). In Annex 3.B.13, the emissions are also given in CollectER format for the years 1990 and 2005.
�� ������ Emissions in 2005 for international transport and national totals
The differences in emissions between navigation and civil aviation are much larger than the differences in fuel use (and derived CO2 emis-sions), and display a poor emission performance for international sea transport. In broad terms, the emission trends shown in Figure 3.46 are similar to the fuel-use development. Minor differences occur for naviga-tion (SO2, NOX and CO2) due to varying amounts of marine gas oil and residual oil, and for civil aviation (NOX) due to yearly variations in LTO/aircraft type (earlier than 2001) and city-pair statistics (2001 on-wards).
���
Residential (1A4b)
0%
Railways (1A3c)
0%
Military (1A5)2%
Civil Aviation (1A3a)
2%
Agr./for. (1A4c)
1%
Industry-Other (1A2f)
1%
Navigation (1A3d)72%
Fisheries (1A4c)22%
���
Residential (1A4b)
1%
Fisheries (1A4c)18%
Navigation (1A3d)21%
Industry-Other (1A2f)
23%
Agr./for. (1A4c)25%
Civil Aviation (1A3a)
1%
Military (1A5)3%
Railways (1A3c)
8%
�����
Residential (1A4b)60%
Fisheries (1A4c)
2%
Navigation (1A3d)
9%
Industry-Other (1A2f)
11%
Agr./for. (1A4c)14%
Civil Aviation (1A3a)
1%
Military (1A5)1%Railways
(1A3c)2%
��
Residential (1A4b)77%
Fisheries (1A4c)
1%
Navigation (1A3d)
5%
Industry-Other (1A2f)
5%
Agr./for. (1A4c)10%
Civil Aviation (1A3a)
1%
Military (1A5)1%
Railways (1A3c)
0%
���������� SO2, NOX, NMVOC and CO emission shares for other mobile sources in 2005
International total 37367 73862 2474 8529 5211 255 37367
113
������������������������� ��������
0
1000
2000
3000
4000
5000
6000
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
�������
Navigation int.(1A3d) Civil Aviation int. (1A3a)
������������������������� ��������
0
10000
20000
30000
40000
50000
60000
70000
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
������
Navigation int.(1A3d) Civil Aviation int. (1A3a)
������������������������� ��������
0
20000
40000
60000
80000
100000
120000
1985
1987
1989
1991
1993
1995
1997
1999
2001
2003
2005
������
Navigation int.(1A3d) Civil Aviation int. (1A3a)
������������������ ��� ��������
0
2000
4000
6000
8000
10000
12000
1985
1987
1989
1991
1993
1995
1997
1999
2001
2003
2005
������
Navigation int.(1A3d)
���������� CO2, SO2, NOX and TSP emissions for international transport 1985-2005
�
������ A���#� �(��� ����!���
The description of methodologies and references for the transport part of the Danish inventory is given in two sections: one for road transport and one for the other mobile sources.
A���#� �(���#���.�������.������#����������For road transport, the detailed methodology is used to make annual es-timates of the Danish emissions, as described in the EMEP/CORINAIR Emission Inventory Guidebook (EMEP/CORINAIR, 2004). The actual calculations are made with a model developed by NERI, using the Euro-pean COPERT III model methodology, and updated fuel use and emis-sion factors from the latest version of COPERT - COPERT IV. The latter model approach is explained in (EMEP/CORINAIR, 2004). In COPERT, fuel use and emission simulations can be made for operationally hot en-gines, taking into account gradually stricter emission standards and emission degradation due to catalyst wear. Furthermore, the emission ef-fects of cold-start and evaporation are simulated.
C��� ��. �����#��� ��(��#����Corresponding to the COPERT III fleet classification, all present and fu-ture vehicles in the Danish fleet are grouped into vehicle classes, sub-classes and layers. The layer classification is a further division of vehicle sub-classes into groups of vehicles with the same average fuel use and emission behaviour, according to EU emission legislation levels. Table 3.23 gives an overview of the different model classes and sub-classes, and the layer level with implementation years are shown in Annex 3.B.1.
114
�� ������ Model vehicle classes and sub-classes, trip speeds and mileage split
New total mileage data for passenger cars, light duty trucks, heavy duty trucks and buses produced by the Danish vehicle inspection programme is used for the years 1985-2004. For 2005, the information for 2004 is used, due to lack of data.
The new Danish mileage data is distributed into annual mileage per first registration year for the different vehicle categories in the inventory, by using the baseline vehicle stock and annual mileage information ob-tained from the Danish Road Directorate (Ekman, 2005). Fleet numbers in total vehicle categories for 2005 has been obtained from the Danish Road Directorate (Ekman, 2006), and data are split into vehicle catego-ries-first registration years, by using the 2004 distribution matrix.
The data set from Ekman (2005) which underpinned the Danish 2004 emission inventory, covers data for the number of vehicles and annual mileage per first registration year for all vehicle sub-classes, and mileage split between urban, rural and highway driving, and the respective aver-age speeds. Additional data for the moped fleet and motorcycle fleet dis-aggregation information is given by The National Motorcycle Associa-tion (Markamp, 2006).
���������� Number of vehicles in sub-classes in 1985-2005
The vehicle numbers per sub-class are shown in Figure 3.47. The engine size differentiation is associated with some uncertainty. The increase in the total number of passenger cars is mostly due to a growth in the num-ber of gasoline cars with engine sizes between 1.4 and 2 litres (from 1990-2002) and an increase in the number of gasoline cars (>2 litres) and diesel cars (< 2 litres). In the later years, there has been a decrease in the num-ber of cars with an engine size smaller than 1.4 litres.
There has been a considerable growth in the number of diesel light-duty vehicles from 1985 to 2005. The two largest truck sizes have also in-creased in numbers during the 1990s. From 2000 onwards, this growth has continued for trucks larger than 32 tonnes, whereas the number of trucks with gross vehicle weights between 16 and 32 tonnes has de-creased slightly.
The number of urban buses has been almost constant between 1985 and 2005. The sudden change in the level of coach numbers from 1994 to 1995 is due to uncertain fleet data.
The reason for the significant growth in the number of mopeds from 1994 to 2002 is the introduction of the so-called Moped 45 vehicle type. For motorcycles, the number of vehicles has grown in general through-out the entire 1985-2005 period. The increase is, however, most visible from the mid-1990s and onwards.
The vehicle numbers are summed up in layers for each year (Figure 3.48) by using the correspondence between layers and first year of registra-tion:
116
∑=
=)(
)(,,
������
�������
���� �� (1)
Where N = number of vehicles, j = layer, y = year, i = first year of regis-tration.
Weighted annual mileages per layer are calculated as the sum of all mileage driven per first registration year divided by the total number of vehicles in the specific layer.
∑
∑
=
=
⋅=
)(
)(,
,
)(
)(,
, ������
�������
��
��
������
�������
��
��
�
��
� (2)
Vehicle numbers and weighted annual mileages per layer are shown in Annex 3.B.1 and 3.B.2 for 1985-2005. The trends in vehicle numbers per layer are also shown in Figure 3.48. The latter figure shows how vehicles complying with the gradually stricter EU emission levels (EURO I, II, III etc.) have been introduced into the Danish motor fleet.
��������� �(�� �����No specific emission legislation exists for CO2. The current EU strategy for reducing CO2 emissions from cars is based on voluntary commit-ments by the car industry, consumer information (car labelling) and fis-cal measures to encourage purchases of more fuel-efficient cars. Under the voluntary commitments, European manufacturers have said they
0��� ��������(�������
0
400
800
1200
1600
2000
1985
1987
1989
1991
1993
1995
1997
1999
2001
2003
2005
������������
�
Euro III
Euro II
Euro I
ECE 15/04
ECE 15/03
ECE 15/02
ECE 15/00-01
PRE ECE
����� ������(�������
0
30
60
90
120
150
1985
1987
1989
1991
1993
1995
1997
1999
2001
2003
2005
������������
� Euro IV
Euro III
Euro II
Euro I
Conventional
3�(��#!������� ��
0
70
140
210
280
350
1985
1987
1989
1991
1993
1995
1997
1999
2001
2003
2005
������������
�Euro V
Euro IV
Euro III
Euro II
Euro I
Conventional
��!�����#�"!���
0
10
20
30
40
50
60
70
1985
1987
1989
1991
1993
1995
1997
1999
2001
2003
2005
������������
�
Euro III
Euro II
Euro I
Conventional
��������� Layer distribution of vehicle numbers per vehicle type in 1985-2005
117
will reduce average emissions from their new cars to 140g CO2/km by 2008, while the Japanese and Korean industries will do so by 2009.
However, the strategy has brought only limited progress towards achieving the target of 120g CO2/km by 2012; from 1995 to 2004 average emissions from new cars sold in the EU-15 fell from 186g CO2/km to 163g CO2/km.
The EU Commission’s review of the strategy has concluded that the vol-untary commitments have not succeeded and that the 120g target will not be met on time without further measures.
The main measures it is proposing in the revised strategy are as follows:
• A legislative framework to reduce CO2 emissions from new cars and vans will be proposed by the EU Commission by the end of this year or at the latest by mid 2008. This will provide the car industry with sufficient lead time and regulatory certainty.
• Average emissions from new cars sold in the EU-27 would be re-quired to reach the 120g CO2/km target by 2012. Improvements in vehicle technology would have to reduce average emissions to no more than 130g/km, while complementary measures would contrib-ute a further emissions cut of up to 10g/km, thus reducing overall emissions to 120g/km. These complementary measures include effi-ciency improvements for car components with the highest impact on fuel consumption, such as tyres and air conditioning systems, and a gradual reduction in the carbon content of road fuels, notably through greater use of biofuels. Efficiency requirements will be intro-duced for these car components.
• For vans, the fleet average emission targets would be 175g by 2012 and 160g by 2015, compared with 201g in 2002.
• Support for research efforts aimed at further reducing emissions from new cars to an average of 95g CO2/km by 2020.
• Measures to promote the purchase of fuel-efficient vehicles, notably through improved labelling and by encouraging Member States that levy car taxes to base them on cars’ CO2 emissions.
• An EU code of good practice on car marketing and advertising to promote more sustainable consumption patterns. The Commission is inviting car manufacturers to sign up to this by mid-2007.
For Euro 1-4 passenger cars and light duty trucks, the chassis dyna-mometer test cycle used in the EU for measuring fuel is the NEDC (New European Driving Cycle), see Nørgaard and Hansen (2004). The test cy-cle is also used also for emissions testing. The NEDC cycle consists of two parts, the first part being a 4-time repetition (driving length: 4 km) of the ECE test cycle. The latter test cycle is the so-called urban driving cy-cle2 (average speed: 19 km/h). The second part of the test is the run-through of the EUDC (Extra Urban Driving Cycle) test driving segment, simulating the fuel use under rural and highway driving conditions. The driving length of EUDC is 7 km at an average speed of 63 km/h. More
2 For Euro 3 and on, the emission approval test procedure was slightly changed. The 40 s engine warm up phase before start of the urban driving cycle was removed.
118
information regarding the fuel measurement procedure can be found in the EU-directive 80/1268/EØF.
For NOx, VOC (NMVOC + CH4), CO and PM, the emissions from road transport vehicles have to comply with the different EU directives listed in Table 3.24. The emission directives distinguish between three vehicle classes according to vehicle reference mass3: Passenger cars and light duty trucks (<1305 kg), light duty trucks (1305-1760 kg) and light duty trucks (>1760 kg).The specific emission limits are shown in Annex 3.B.3.
3 Reference mass: net vehicle weight + mass of fuel and other liquids + 100 kg.
119
�� ������ Simplified overview of the existing EU emission directives for road transport vehi-cles
a,b,c,d: Expert judgement suggest that Danish vehicles enter into the traffic before EU di-rective first registration dates. The effective inventory starting years are a: 1970; b: 1979; c: 1981; d: 1986. e: The directive came into force in Denmark in 1991 (EU starting year: 1993.
In practice, the emissions from vehicles in traffic are different from the legislation limit values and, therefore, the latter figures are considered to be too inaccurate for total emission calculations. A major constraint is that the emission approval test conditions reflect only to a small degree the large variety of emission influencing factors in the real traffic situa-tion, such as cumulated mileage driven, engine and exhaust after treat-ment maintenance levels and driving behaviour.
Vehicle category Emission layer EU directive First reg. year
start
Passenger cars (gasoline) PRE ECE 0
ECE 15/00-01 70/220 - 74/290 1972a
ECE 15/02 77/102 1981b
ECE 15/03 78/665 1982c
ECE 15/04 83/351 1987d
Euro I 91/441 1991e
Euro II 94/12 1997
Euro III 98/69 2001
Euro IV 98/69 2006
Passenger cars (diesel and LPG) Conventional 0
ECE 15/04 83/351 1987d
Euro I 91/441 1991e
Euro II 94/12 1997
Euro III 98/69 2001
Euro IV 98/69 2006
Light duty trucks (gasoline and Conventional 0
ECE 15/00-01 70/220 - 74/290 1972a
ECE 15/02 77/102 1981b
ECE 15/03 78/665 1982c
ECE 15/04 83/351 1987d
Euro I 93/59 1995
Euro II 96/69 1999
Euro III 98/69 2002
Euro IV 98/69 2007
Heavy duty vehicles Conventional 0
Euro 0 88/77 1991
Euro I 91/542 1994
Euro II 91/542 1997
Euro III 1999/96 2002
Euro IV 1999/96 2007
Euro V 1999/96 2010
Mopeds Conventional 0
Euro I 97/24 2000
Euro II 2002/51 2004
Motor cycles Conventional 0
Euro I 97/24 2000
Euro II 2002/51 2004
Euro III 2002/51 2007
120
Therefore, in order to represent the Danish fleet and to support average national emission estimates, emission factors must be chosen which de-rive from numerous emissions measurements, using a broad range of real world driving patterns and a sufficient number of test vehicles. It is similar important to have separate fuel use and emission data for cold-start emission calculations and gasoline evaporation (hydrocarbons).
For heavy-duty vehicles (trucks and buses), the emission limits are given in g/kWh and the measurements are carried out for engines in a test bench, using the EU ESC (European Stationary Cycle) and ETC (Euro-pean Transient Cycle) test cycles, depending on the Euro norm and ex-haust gas after-treatment system installed. A description of the test cy-cles is given by Nørgaard and Hansen, 2004). Measurement results in g/kWh from emission approval tests cannot be directly used for inven-tory work. Instead, emission factors used for national estimates must be transformed into g/km, and derived from a sufficient number of meas-urements which represent the different vehicle size classes, Euro engine levels and real world variations in driving behaviour.
%!� �!����#���������.�������Trip-speed dependent basis factors for fuel use and emissions are taken from the COPERT model using trip speeds as shown in Table 3.23. The factors are listed in Annex 3.B.4. For EU emission levels not represented by actual data, the emission factors are scaled according to the reduction factors given in Annex 3.B.5. For further explanation, see EMEP/CORINAIR (2004) or Illerup et al. (2003).
The fuel use and emission factors used in the Danish inventory come from the COPERT IV model. The scientific basis for COPERT IV is fuel use and emission information from the European 5th framework re-search projects ARTEMIS and Particulates. In cases where no updates are made for vehicle categories and fuel use/emission components, COPERT IV still uses COPERT III data; the source for these data are various European measurement programmes. In general the COPERT data are transformed into trip-speed dependent fuel use and emission factors for all vehicle categories and layers.
For passenger cars, real measurement results are behind the emission factors for Euro 1-4 gasoline vehicles and Euro 1-3 diesel vehicles (up-dated figures), and those earlier (COPERT III data). For light duty trucks the measurements represent Euro 1 and prior vehicle technologies from COPERT III. For mopeds and motorcycles, updated fuel use and emis-sion figures are behind the conventional and Euro 1-3 technologies.
The experimental basis for heavy-duty trucks and buses is updated computer simulated emission factors for Euro 0-V engines. In COPERT IV the number of heavy duty vehicle categories has increased substan-tionally, and from the traffic data side is not possible to support all these new vehicle categories with consistent fleet and mileage data. Thus, the COPERT III vehicle size classification still remains as the Danish inven-tory basis for heavy duty vehicles.
However, in order to use the new COPERT IV fuel use and emission in-formation, the decision is to calculate average fuel use and emission fac-tors per technology level (Euro O-V) from COPERT IV. The average fac-
121
tors comprise the specific COPERT IV size categories in overlap with a given COPERT III size category. Next, these average COPERT IV factors are scaled with the ratio of fuel use factors between COPERT III and "av-erage COPERT IV" in order to end up with vehicle sizes corresponding to COPERT III weight classes.
For all vehicle categories/technology levels not represented by meas-urements, the emission factors are produced by using reduction factors. The latter factors are determined by assessing the EU emission limits and the relevant emission approval test conditions, for each vehicle type and Euro class.
�������������.�������For three-way catalyst cars the emissions of NOX, NMVOC and CO gradually increase due to catalyst wear and are, therefore, modified as a function of total mileage by the so-called deterioration factors. Even though the emission curves may be serrated for the individual vehicles, on average, the emissions from catalyst cars stabilise after a given cut-off mileage is reached due to OBD (On Board Diagnostics) and the Danish inspection and maintenance programme.
For each forecast year, the deterioration factors are calculated per first registration year by using deterioration coefficients and cut-off mileages, as given in EMEP/CORINAIR (2004), for the corresponding layer. The deterioration coefficients are given for the two driving cycles: ”Urban Driving Cycle” (UDF) and ”Extra Urban Driving Cycle” (EUDF: urban and rural), with trip speeds of 19 and 63 km/h, respectively.
Firstly, the deterioration factors are calculated for the corresponding trip speeds of 19 and 63 km/h in each case determined by the total cumu-lated mileage less than or exceeding the cut-off mileage. The Formulas 3 and 4 show the calculations for the ”Urban Driving Cycle”:
���������� +⋅= , MTC < UMAX (3)
����������� +⋅= , MTC >= UMAX (4)
where UDF is the urban deterioration factor, UA and UB the urban dete-rioration coefficients, MTC = total cumulated mileage and UMAX urban cut-off mileage.
In the case of trip speeds below 19 km/h the deterioration factor, DF, equals UDF, whereas for trip speeds exceeding 63 km/h, DF=EUDF. For trip speeds between 19 and 63 km/h the deterioration factor, DF, is found as an interpolation between UDF and EUDF. Secondly, the dete-rioration factors, one for each of the three road types, are aggregated into layers by taking into account vehicle numbers and annual mileage levels per first registration year:
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122
where DF is the deterioration factor.
����������#�.!� �!���.�������(����Emissions and fuel-use results for operationally hot engines are calcu-lated for each year and for layer and road type. The procedure is to com-bine fuel use and emission factors (and deterioration factors for catalyst vehicles), number of vehicles, annual mileage levels and the relevant road-type shares given in Table 3.23. For non-catalyst vehicles this yields:
����������� ����� ,,,,,, ⋅⋅⋅= (6)
Here E = fuel use/emission, EF = fuel use/emission factor, S = road type share and k = road type.
For catalyst vehicles the calculation becomes:
�������������� ������� ,,,,,,,, ⋅⋅⋅⋅= (7)
�-��������������#�.!� �!���.����� #��(����Extra emissions of SO2, NOX, NMVOC, CH4, CO, CO2, PM and fuel con-sumption from cold start are simulated separately. In terms of cold start data no updates are made to the COPERT IV methodology, and the cal-culation approach is the same as in COPERT III. Each trip is associated with a certain cold-start emission level and is assumed to take place un-der urban driving conditions. The number of trips is distributed evenly across the months. First, cold emission factors are calculated as the hot emission factor times the cold:hot emission ratio. Secondly, the extra emission factor during cold start is found by subtracting the hot emission factor from the cold emission factor. Finally, this extra factor is applied on the fraction of the total mileage driven with a cold engine (the β-factor) for all vehicles in the specific layer.
The cold:hot ratios depend on the average trip length and the monthly ambient temperature distribution. The Danish temperatures for 2005, 2004, 2000-2003, 1990-1999 and 1980-1989 are given in Cappelen et al. (2006), Cappelen et al. (2005) and Cappelen (2004, 2000 and 2003). The cold:hot ratios are equivalent for gasoline fuelled conventional passenger cars and vans and for diesel passenger cars and vans, respectively, see Ntziachristos et al. (2000). For conventional gasoline and all diesel vehi-cles the extra emissions become:
)1(,,,,, −⋅⋅⋅⋅= �������� ��������� β (8)
Where CE is the cold extra emissions, β = cold driven fraction, CEr = Cold:Hot ratio.
For catalyst cars, the cold:hot ratio is also trip speed dependent. The ratio is, however, unaffected by catalyst wear. The Euro I cold:hot ratio is used for all future catalyst technologies. However, in order to comply with gradually stricter emission standards, the catalyst light-off temperature must be reached in even shorter periods of time for future EURO stan-dards. Correspondingly, the β-factor for gasoline vehicles is reduced step-wise for Euro II vehicles and their successors.
123
For catalyst vehicles the cold extra emissions are found from:
���������������������.����(��� ������� ���For each year, evaporative emissions of hydrocarbons are simulated in the forecast model as hot and warm running losses, hot and warm soak loss and diurnal emissions. For evaporation, no updates are made to the COPERT IV methodology, and the calculation approach is the same as in COPERT III. All emission types depend on RVP (Reid Vapour Pressure) and ambient temperature. The emission factors are shown in Ntziachris-tos et al. (2000).
Running loss emissions originate from vapour generated in the fuel tank while the vehicle is running. The distinction between hot and warm run-ning loss emissions depends on engine temperature. In the model, hot and warm running losses occur for hot and cold engines, respectively. The emissions are calculated as annual mileage (broken down into cold and hot mileage totals using the β-factor) times the respective emission factors. For vehicles equipped with evaporation control (catalyst cars), the emission factors are only one tenth of the uncontrolled factors used for conventional gasoline vehicles.
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⋅+⋅−⋅⋅= ββ (10)
where R is running loss emissions and HR and WR are the hot and warm running loss emission factors, respectively.
In the model, hot and warm soak emissions for carburettor vehicles also occur for hot and cold engines, respectively. These emissions are calcu-lated as number of trips (broken down into cold and hot trip numbers using the β-factor) times respective emission factors:
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where SC is the soak emission, ltrip = the average trip length, and HS and WS are the hot and warm soak emission factors, respectively. Since all catalyst vehicles are assumed to be carbon canister controlled, no soak emissions are estimated for this vehicle type. Average maximum and minimum temperatures per month are used in combination with diurnal emission factors to estimate the diurnal emissions from uncontrolled ve-hicles Ed(U):
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Each year’s total is the sum of each layer’s running loss, soak loss and diurnal emissions.
124
%!� �!���"� ����The calculated fuel use in COPERT III must equal the statistical fuel sale and energy forecast totals from the Danish Energy Authority (DEA, 2006) according to the UNFCCC and UNECE emissions reporting for-mat. The standard approach to achieve a fuel balance in annual emission inventories is to multiply the annual mileage with a fuel balance factor derived as the ratio between simulated and statistical fuel figures for gasoline and diesel, respectively. This method is also used in the present model.
In the figures 3.49 and 3.50 the COPERT IV:DEA gasoline and diesel fuel use ratios are shown for fuel sales and fuel consumption from 1985-2005. The fuel consumption figures are related to the traffic on Danish roads.
For gasoline vehicles all mileage numbers are equally scaled in order to obtain gasoline fuel equilibrium, and hence the gasoline mileage factor used is the reciprocal value of the COPERT IV:DEA gasoline fuel use ra-tio.
����������� DEA:NERI Fuel ratios and diesel mileage adjustment factor based on DEA fuel consumption data and NERI fuel consumption estimates
125
For diesel the fuel balance is made by adjusting the mileage for light and heavy-duty vehicles and buses, given that the mileage and fuel con-sumption factors for these vehicles are regarded as the most uncertain parameters in the diesel engine emission simulations. Consequently, the diesel mileage factor used is slightly higher than the reciprocal value of the COPERT IV:DEA diesel fuel use ratio.
From the Figures 3.49 and 3.50 it appears that the inventory fuel balances for gasoline and diesel would be improved, if the DEA statistical figures for fuel consumption were used instead of fuel sale numbers. The fuel difference for diesel is, however, still significant. The reasons for this in-accuracy are a combination of the uncertainties related to COPERT IV fuel use factors, allocation of vehicle numbers in sub-categories, annual mileage, trip speeds and mileage splits for urban, rural and highway driving conditions.
For future inventories it is intended to use improved fleet and mileage data and improved data for trip speed and mileage split for urban, rural and highway driving. The update of road traffic fleet and mileage data will be made as soon as this information is provided from the Danish Ministry of Transport and Energy in a COPERT IV model input format.
The final fuel use and emission factors are shown in Annex 3.B.6 for 1990-2005. The total fuel use and emissions are shown in Annex 3.B.7, per vehicle category and as grand totals, for 1990-2005 (and CRF format in Annex 3.B.14). In Annex 3.B.13, fuel-use and emission factors as well as total emissions are given in CollectER format for 1990 and 2005.
In Table 3.25, the aggregated emission factors for CO2, CH4 and N2O are shown per fuel type for the Danish road transport.
126
��������� Fuel-specific emission factors for CO2, CH4 and N2O for road transport in Denmark
�A���#� �(�����#���.�������.����������"� ����!�����Other mobile sources are divided into several subsectors: sea transport, fishery, air traffic, railways, military, and working machinery and mate-riel in the industry, forestry, agriculture and household and gardening sectors. The emission calculations are made using the detailed method as described in the EMEP/CORINAIR Emission Inventory Guidebook (EMEP/CORINAIR, 2004) for air traffic, off-road working machinery and equipment, and ferries, while for the remaining sectors the simple method is used.
4 References. CO2: Country specific: CH4 and N2O: COPERT III
7������..���The activity data for air traffic consists of air traffic statistics provided by the Danish Civil Aviation Agency (CAA-DK) and Copenhagen Airport. For 2001 onwards, per flight records are provided by CAA-DK as data for aircraft type, and origin and destination airports. For inventory years prior to 2001, detailed LTO/aircraft type statistics are obtained from Co-penhagen Airport (for this airport only), while information of total take-off numbers for other Danish airports is provided by CAA-DK. Fuel sta-tistics for jet fuel use and aviation gasoline are obtained from the Danish energy statistics (DEA, 2006).
Prior to emission calculations, the aircraft types are grouped into a smaller number of representative aircraft groups, for which fuel use and emission data exist in the EMEP/CORINAIR databank. In this proce-dure, actual aircraft types are classified according to their overall aircraft type (jets, turbo props, helicopters and piston engines). Secondly, infor-mation on the aircraft MTOM (Maximum Take Off Mass) and number of engines are used to append a representative aircraft to the aircraft type in question. A more thorough explanation is given in Winther (2001a, b).
������#�&����(����������#��>!������Non-road working machinery and equipment are used in agriculture, forestry and industry, for household/gardening purposes and in inland waterways (recreational craft). Information on the number of different types of machines, their repective load factors, engine sizes and annual working hours has been provided by Winther et al. (2006). The stock de-velopment from 1985-2005 for the most important types of machinery are shown in Figures 3.51-3.58 below. The stock data are also listed in Annex 3.B.10, together with figures for load factors, engine sizes and annual working hours. As regards stock data for the remaining machinery types, please refer to (Winther et al., 2006).
For agriculture, the total number of agricultural tractors and harvesters per year are shown in the Figures 3.51-3.52, respectively. The Figures clearly show a decrease in the number of small machines, these being re-placed by machines in the large engine-size ranges.
128
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129
The tractor and harvester developments towards fewer vehicles and lar-ger engines, shown in Figure 3.53, are very clear. From 1985 to 2005, trac-tor and harvester numbers decrease by around 20 % and 50 %, respec-tively, whereas the average increase in engine size for tractors is 16 %, and more than 100 % for harvesters, in the same time period.
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The most important machinery types for industrial use are different types of construction machinery and fork lifts. The Figures 3.54 and 3.55 show the 1985-2005 stock development for specific types of construction machinery and diesel fork lifts. Due to lack of data, the construction ma-chinery stock for 1990 is used also for 1985-1989. For most of the machin-
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130
ery types there is an increase in machinery numbers from 1990 onwards, due to increased construction activities. It is assumed that track type ex-cavators/ wheel type loaders (0-5 tonnes), and telescopic loaders first en-ter into use in 1991 and 1995, respectively.
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���������� 1985-2005 stock development for specific types of construction machinery
131
The emission level shares for tractors, harvesters, construction machin-ery and diesel fork lifts are shown in Figure 3.56, and present an over-view of the penetration of the different pre-Euro engine classes, and en-gine stages complying with the gradually stricter EU stage I and II emis-sion limits. The average lifetimes of 30, 25, 20 and 10 years for tractors, harvesters, fork lifts and construction machinery, respectively, influence the individual engine technology turn-over speeds.
The EU emission directive Stage I and II implementation years relate to engine size, and for all four machinery groups the emission level shares for the specific size segments will differ slightly from the picture shown in Figure 3.56. Due to scarce data for construction machinery, the emis-sion level penetration rates are assumed to be linear and the general technology turnover pattern is as shown in Figure 3.56.
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132
The 1985-2005 stock development for the most important household and gardening machinery types is shown in Figure 3.57. The 2004 stock data are repeated for 2005, since no new fleet information has been obtained for this sector of non road machinery.
For lawn movers and cultivators, the machinery stock remains the same for all years, whereas the stock figures for riders, chain saws, shrub clearers, trimmers and hedge cutters increase from 1990 onwards. The yearly stock increases, in most cases, become larger after 2000. The life-times for gasoline machinery are short and, therefore, there new emis-sion levels (not shown) penetrate rapidly.
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133
Figure 3.58 shows the development in numbers of different recreational craft from 1985-2005. As for the residential sector, the 2004 stock data for recreational craft are repeated for 2005, since no new fleet information has been obtained.
For diesel boats, increases in stock and engine size are expected during the whole period, except for the number of motor boats (< 27 ft.) and the engine sizes for sailing boats (<26 ft.), where the figures remain un-changed. A decrease in the total stock of sailing boats (<26 ft.) by 21% and increases in the total stock of yawls/cabin boats and other boats (<20 ft.) by around 25% are expected. Due to a lack of information specific to Denmark, the shifting rate from 2-stroke to 4-stroke gasoline engines is based on a German non-road study (IFEU, 2004).
��������� Stock development 1985-2005 for the most important household and gardening machinery types
134
�%�������A new Danish research project has carried out detailed fuel use and emission calculations for Danish ferries (Winther, 2007b). Table 3.26 lists the most important domestic ferry routes in Denmark in the period 1990-2005. For these ferry routes the following detailed traffic and technical data have been gathered: Ferry name, year of service, engine size (MCR), engine type, fuel type, average load factor, auxiliary engine size and sail-ing time (single trip).
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��������� 1985-2005 Stock and engine size development for recreational craft
135
�� ������� Ferry routes comprised in the present project
Ferry service Service period
Halsskov-Knudshoved 1990-1999
Hundested-Grenaa 1990-1996
Kalundborg-Juelsminde 1990-1996
Kalundborg-Samsø 1990-
Kalundborg-Århus 1990-
Korsør-Nyborg, DSB 1990-1997
Korsør-Nyborg, Vognmandsruten 1990-1999
København-Rønne 1990-2004
Køge-Rønne 2004-
Sjællands Odde-Ebeltoft 1990-
Sjællands Odde-Århus 1999-
Tårs-Spodsbjerg 1990-
The number of round trips per ferry route is shown in Figure 3.59. The traffic data are also listed in Annex 3.b.11, together with different ferry specific technical and operational data. There is a lack of historical traffic data for 1985-1989, and hence, data for 1990 is used for these years, to support the fuel use and emission calculations.
For each ferry, Annex 3.B.12 lists the relevant information as regards ferry route, name, year of service, engine size (MCR), engine type, fuel type, average load factor, auxillary engine size and sailing time (single trip).
0
2000
4000
6000
8000
10000
12000
14000
16000
1990
1992
1994
1996
1998
2000
2002
2004
��!#�������1���2
Halsskov-Knudshoved
Korsør-Nyborg, DSB
Tårs-Spodsbjerg
Korsør-Nyborg,Vognmandsruten
Sjællands Odde-Ebeltoft
Hundested-Grenaa
0
1000
2000
3000
4000
5000
6000
1990
1992
1994
1996
1998
2000
2002
2004
������������� �
Kalundborg-Århus
Kalundborg-Samsø
København-Rønne
Kalundborg-Juelsminde
Køge-Rønne
Sjællands Odde-Århus
���������� No. of round trips for the most important ferry routes in Denmark 1990-2005
136
It is seen from Table 2.1 (and Figure 3.59) that several ferry routes were closed in the time period from 1996-1998, mainly due to the opening of the Great Belt Bridge (connecting Zealand and Funen) in 1997. Hunde-sted-Grenaa and Kalundborg-Juelsminde was closed in 1996, Korsør-Nyborg (DSB) closed in 1997, and Halsskov-Knudshoved and Korsør-Nyborg (Vognmandsruten) was closed in 1998. The ferry line Køben-havn-Rønne was replaced by Køge-Rønne in 2004 and from 1999 a new ferry connection was opened between Sjællands Odde and Århus.
6������������The activity data for military, railways, sea transport and fishery consists of fuel use information from DEA (2006). For sea transport, the basis is fuel sold in Danish ports and, depending on the destination of the ves-sels in question the traffic, is defined as either national or international, as prescribed by the IPCC guidelines. The part of fuel in national sea transport not being used by domestic ferries, is found as the DEA figures minus the bottom-up fuel use estimate for ferries (for gas oil and heavy fuel in each case), and is classified as fuel used by other national sea transport.
For all sectors, fuel-use figures are given in Annex 3.B.13 for the years 1990 and 2005 in CollectER format.
�������� �(�� �����For the engines used by other mobile sources, no legislative limits exist for specific fuel use. And no legislative limits exist for the emissions of CO2 which are directly fuel dependent. The engines, however, do have to comply with the emission legislation limits agreed by the EU and, except for ships, the VOC emission limits influence the emissions of CH4, these forming part of total VOC.
For non-road working machinery and equipment, and recreational craft and railway locomotives/motor cars, the emission directives list specific emission limit values (g/kWh) for CO, VOC, NOx (or VOC + NOx) and TSP, depending on engine size (kW for diesel, ccm for gasoline) and date of implementation (referring to engine market date).
For diesel, the directives 97/68 and 2004/26 relate to non-road machin-ery other than agricultural and forestry tractors, and the directives have different implementation dates for machinery operating under transient and constant loads. The latter directive also comprises emission limits for railway machinery. For tractors the relevant directives are 2000/25 and 2005/13. For gasoline, the directive 2002/88 distinguishes between hand-held (SH) and not hand-held (NS) types of machinery.
For engine type approval, the emissions (and fuel use) are measured us-ing various test cycles (ISO 8178). Each test cycle consists of a number of measurement points for specific engine loads during constant operation. The specific test cycle used depends on the machinery type in question and the test cycles are described in more details in the directives.
137
�� ������� Overview of EU emission directives relevant for diesel fuelled non-road machinery
Stage/Engine CO VOC NOx VOC+NOx PM Diesel machinery Tractors
size [kW] Implement. date EU Implement.
[g/kWh] EU Directive Transient Constant directive date
�� ������ Overview of the EU Emission Directive 2002/88 for gasoline fuelled non-road machinery
For recreational craft, Directive 2003/44 comprises the emission legisla-tion limits for diesel engines, and for 2-stroke and 4-stroke gasoline en-gines, respectively. The CO and VOC emission limits depend on engine size (kW) and the inserted parameters presented in the calculation for-mulas in Table 3.29. For NOx, a constant limit value is given for each of the three engine types. For TSP, the constant emission limit regards die-sel engines only.
�� ������� Overview of the EU Emission Directive 2003/44 for recreational craft
�� ������� Overview of the EU Emission Directive 2004/26 for railway locomotives and motorcars
Aircraft engine emissions of NOx, CO, VOC and smoke are regulated by ICAO (International Civil Aviation Organization). The legislation is rele-
Category Engine size
[ccm]
CO
[g/kWh]
HC
[g/kWh]
NOx
[g/kWh]
HC+NOx
[g/kWh]
Implemen-tation date
Stage I
Hand held SH1 S<20 805 295 5.36 - 1/2 2005
SH2 20=<S<50 805 241 5.36 - 1/2 2005
SH3 50=<S 603 161 5.36 - 1/2 2005
Not hand held SN3 100=<S<225 519 - - 16.1 1/2 2005
SN4 225=<S 519 - - 13.4 1/2 2005
Stage II
Hand held SH1 S<20 805 - - 50 1/2 2008
SH2 20=<S<50 805 - - 50 1/2 2008
SH3 50=<S 603 - - 72 1/2 2009
Not hand held SN1 S<66 610 - - 50 1/2 2005
SN2 66=<S<100 610 - - 40 1/2 2005
SN3 100=<S<225 610 - - 16.1 1/2 2008
SN4 225=<S 610 - - 12.1 1/2 2007
Engine type Impl. date CO=A+B/Pn HC=A+B/Pn NOx TSP
vant for aircraft engines with a rated engine thrust larger than 26.7 kN. A further description of the emission legislation and emission limits is given in ICAO Annex 16 (1993).
��������.�������The CO2 emission factors are country-specific and come from the DEA. The N2O emission factors are taken from the EMEP/CORINAIR guide-book (EMEP/CORINAIR, 2004).
For military ground material, aggregated CH4 emission factors for gaso-line and diesel are derived from the road traffic emission simulations. The CH4 emission factors for railways are derived from specific Danish VOC measurements from the Danish State Railways (Næraa, 2005) and a NMVOC/CH4 split, based on own judgment.
For agriculture, forestry, industry, household gardening and inland wa-terways, the VOC emission factors are derived from various European measurement programmes and the current EU emission legislation; see IFEU (2004) and Winther et al. (2006). The NMVOC/CH4 split is taken from USEPA (2004). The baseline emission factors are shown in Annex 3.B.9.
For national sea transport and fisheries, the VOC emission factors come from Trafikministeriet (2000). The NMVOC/CH4 split is taken from EMEP/CORINAIR (2004). The baseline emission factors are shown in Annex 3.B.12.
The CH4 emission factors for domestic aviation come from the EMEP/CORINAIR guidebook, see EMEP/CORINAIR (2004).
For all sectors, emission factors for the years 1990 and 2004 are given in CollectER format in Annex 3.B.13. Table 3.31 shows the aggregated emis-sion factors for CO2, CH4 and N2O in 2005 used to calculate the emis-sions from other mobile sources in Denmark.
%�������.���#�����������)��������� ��#���#�(��� ��������������.��������#���������The emission effects of engine wear are taken into account for diesel and gasoline engines by using the so/called deterioration factors. For diesel engines alone, transient factors are used in the calculations, to account for the emission changes caused by varying engine loads. The evapora-tive emissions of NMVOC are estimated for gasoline fuelling and tank evaporation. The factors for deterioration, transient loads and gasoline evaporation are taken from IFEU(2004), and are shown in Annex 3.B.9. For more details regarding the use of these factors, please refer to para-graph 3.1.4 or Winther et al. (2006).
140
�� ������� Fuel-specific emission factors for CO2, CH4 and N2O for other mobile sources in Denmark
5 References. CO2: Country-specific. N2O: EMEP/CORINAIR. CH4: Railways: DSB/NERI; Agriculture/Forestry/Industry/Household-Gardening: IFEU/USEPA; National sea traffic/Fishing/International sea traffic: Trafikministeriet/EMEP-CORINAIR; domestic and international aviation: EMEP/CORINAIR.
SNAP ID CRF ID Category Fuel type Mode Emission factors5
CH4 [g/GJ] CO2 [kg/GJ] N2O [g/GJ]
801 1A5 Military Diesel 6.44 74 5.66
801 1A5 Military Jet fuel < 3000 ft 2.65 72 2.30
801 1A5 Military Jet fuel > 3000 ft 2.65 72 2.30
801 1A5 Military Gasoline 22.26 73 11.50
801 1A5 Military Aviation gasoline 21.90 73 2.00
802 1A3c Railways Diesel 2.88 74 2.04
803 1A3d Inland waterways Diesel 2.76 74 2.97
803 1A3d Inland waterways Gasoline 55.94 73 1.13
80402 1A3d National sea traffic Residual oil 2.01 78 4.89
80402 1A3d National sea traffic Diesel 1.55 74 4.68
80402 1A3d National sea traffic Kerosene 7.00 72 0.00
80404 Memo item International sea traffic Diesel 1.70 74 4.68
80501 1A3a Air traffic, other airports Jet fuel Dom. < 3000 ft 3.36 72 18.05
80501 1A3a Air traffic, other airports Aviation gasoline 21.90 73 2.00
80502 Memo item Air traffic, other airports Jet fuel Int. < 3000 ft 1.79 72 8.48
80502 Memo item Air traffic, other airports Aviation gasoline 21.90 73 2.00
80503 1A3a Air traffic, other airports Jet fuel Dom. > 3000 ft 2.62 72 2.30
80504 Memo item Air traffic, other airports Jet fuel Int. > 3000 ft 0.71 72 2.30
806 1A4c Agriculture Diesel 1.50 74 3.13
806 1A4c Agriculture Gasoline 132.74 73 1.57
807 1A4c Forestry Diesel 0.94 74 3.21
807 1A4c Forestry Gasoline 54.12 73 0.42
808 1A2f Industry Diesel 1.69 74 3.08
808 1A2f Industry Gasoline 103.02 73 1.41
808 1A2f Industry LPG 7.69 65 3.50
809 1A4b Household and gardening Gasoline 71.57 73 1.17
80501 1A3a Air traffic, Copenhagen air-port
Jet fuel Dom. < 3000 ft 4.65
72 9.84
80501 1A3a Air traffic, Copenhagen air-port
Aviation gasoline 21.90
73 2.00
80502 Memo item Air traffic, Copenhagen air-port
Jet fuel Int. < 3000 ft 4.18
72 4.07
80502 Memo item Air traffic, Copenhagen air-port
Aviation gasoline 21.90
73 2.00
80503 1A3a Air traffic, Copenhagen air-port
Jet fuel Dom. > 3000 ft 2.30
72 2.30
80504 Memo item Air traffic, Copenhagen air-port
Jet fuel Int. > 3000 ft 1.15
72 2.30
141
����/� '� �! ���������#�
7������..���For aviation, the estimates are made separately for landing and take- off (LTOs < 3000 ft), and cruising (> 3000 ft). From 2001, the estimates are made on a city-pair level by combining activity data and emission factors and subsequently grouping the emission results intodomestic and inter-national totals. The overall fuel precision in the model is around 0.8, de-rived as the fuel ratio of model estimates to statistical sales. The fuel dif-ference is accounted for by adjusting the cruise fuel consumption and emissions in the model, according to the domestic and international cruise fuel shares.
Prior to 2001, the calculation scheme involved firstly estimation of each year’s fuel use and emissions for LTO. Secondly, the total cruise fuel use was found, year for year, as the statistical fuel use total minus the calcu-lated fuel use for LTO. Lastly, the cruise fuel use was split into a domes-tic and an international part, by using the results from a Danish city-pair emission inventory in 1998 (Winther, 2001a). For more details of the lat-ter fuel allocation procedure, see Winther (2001b).
������#�&����(����������#����������� ����.��Prior to adjustments for deterioration effects and transient engine opera-tions, the fuel use and emissions in year X, for a given machinery type, engine size and engine age, are calculated as:
����������������� ����� ���� ,,,,,,,)( ⋅⋅⋅⋅= (13)
where EBasis = fuel use/emissions in the basic situation, N = number of engines, HRS = annual working hours, P = average rated engine size in kW, LF = load factor, EF = fuel use/emission factor in g/kWh, i = ma-chinery type, j = engine size, k = engine age, y = engine-size class and z = emission level. The basic fuel use and emission factors are shown in An-nex 3.B.9.
The deterioration factor for a given machinery type, engine size and en-gine age in year X depends on the engine-size class (only for gasoline), y, and the emission level, z. The deterioration factors for diesel and gaso-line 2-stroke engines are found from:
��
�
���
��� ����
���� ,
,,,, )( ⋅= (14)
where DF = deterioration factor, K = engine age, LT = lifetime, i = ma-chinery type, j = engine size, k = engine age, y = engine-size class and z = emission level.
For gasoline 4-stroke engines the deterioration factors are calculated as:
��
�
���
��� ����
���� ,
,,,, )( ⋅= (15)
142
The deterioration factors inserted in (14) and (15) are shown in Annex 3.B.9. No deterioration is assumed for fuel use (all fuel types) or for LPG engine emissions and, hence, DF = 1 in these situations.
The transient factor for a given machinery type, engine size and engine age in year X, relies only on emission level and load factor, and is de-nominated as:
���� ����� =)(,, (16)
Where i = machinery type, j = engine size, k = engine age and z = emis-sion level.
The transient factors inserted in (16) are shown in Annex 3.B.9. No tran-sient corrections are made for gasoline and LPG engines and,hence, TFz = 1 for these fuel types.
The final calculation of fuel use and emissions in year X for a given ma-chinery type, engine size and engine age, is the product of the expres-sions 13-16:
The evaporative hydrocarbon emissions from fuelling are calculated as:
�������������������� ����� ,,, ⋅= (18)
Where EEvap,fueling, = hydrocarbon emissions from fuelling, i = machinery type, FC = fuel consumption in kg, EFEvap,fueling = emission factor in g NMVOC/kg fuel.
For tank evaporation, the hydrocarbon emissions are found from:
������������� ���� ,tan,,tan, ⋅= (19)
Where EEvap,tank,i = hydrocarbon emissions from tank evaporation, N = number of engines, i = machinery type and EFEvap,fueling = emission factor in g NMVOC/year.
%������)����������� ���������������#�.��������The fuel use and emissions in year X, for ferries are calculated as:
∑ ⋅⋅⋅⋅⋅=�
��������� ��������� ,,,)( (20)
Where E = fuel use/emissions, N = number of round trips, T = sailing time per round trip in hours, S = ferry share of ferry service round trips, P = engine size in kW, LF = engine load factor, EF = fuel use/emission factor in g/kWh, i = ferry service, j = ferry, k = fuel type, l = engine type, y = engine year.
For the remaining navigation categories, the emissions are calculated us-ing a simplified approach:
143
∑=�
����� ������ ,,,)( (21)
Where E = fuel use/emissions, EC = energy consumption, EF = fuel use/emission factor in g/kg fuel, i = category (ferry boats, other national sea, fishery, international sea), k = fuel type, l = engine type, y = average engine year.
The emission factor inserted in (21) is found as an average of the emis-sion factors representing the engine ages which are comprised by the av-erage lifetime in a given calculation year, X:
��
�������
�����
��
��� ��
����
,
,
,,
∑−=
== (22)
6������������For military and railways, the emissions are estimated with the simple method using fuel-related emission factors and fuel use from the DEA:
����� ⋅= (23)
where E = emission, FC = fuel consumption and EF = emission factor. The calculated emissions for other mobile sources are shown in Collec-tER format in Annex 3.B.13 for the years 1990 and 2005 and as time-series 1985-2005 in Annex 3.B.14 (CRF format).
%!� �!���"� ����.�������� ��������������For national sea transport, bottom-up fuel use estimates are calculated for ferries, and rough fuel use estimates are also produced for ferry boats. For inventory years, when the fuel sum (heavy fuel oil and gas oil treated separately) is above the reported fuel totals from the DEA statis-tics, adjustments must then be made to the fuel use and emission results, in order to fulfil the convention rules. The adjustments are made per fuel type by simply scaling the results with the DEA:Calculated fuel use ratio.
Figure 3.60 shows the ratios between the calculated fuel use and the sta-tistical fuel sales from 1990-2005, split into fuel type and as totals for na-tional sea transport.
144
For gas oil, the surplus of calculated fuel use is very big for the years un-til 1992, and from 1997-1999; only for the years 2002, 2003 and 2005 the calculated gas oil fuel total becomes lower than statistical sales. For heavy fuel, only the years 1994-1996 show a surplus of calculated fuel, and from 1998 onwards the calculated fuel use is significantly lower than statistical sales. In terms of total fuel use, the calculated fuel use surplus tends to increase until 1998, followed by a decreasing trend up to today’s situation.
There are many potential reasons for the fuel use differences. From the fuel supplier’s side, errors such as sector misallocation or incorrect fuel type descriptions may cause disturbances in the fuel balance, and gen-eral calculation uncertainties may bring a certain displacement in the es-timated fuel use trend. At the moment it is, however, not possible to de-termine what are the exact reasons for the very fluctuating fuel use ra-tios. A more thorough discussion is given in Winther (2007b).
������#��������J���7�������������#����������������For diesel and LPG, the non-road fuel use estimated by NERI is partly covered by the fuel-use amounts in the following DEA sectors: agricul-ture and forestry, market gardening, and building and construction. The remaining quantity of non-road diesel and LPG is taken from the DEA industry sector.
For gasoline, the DEA residential sector, together with the DEA sectors mentioned for diesel and LPG, contribute to the non-road fuel use total. In addition, a certain amount of fuel from road transport is needed to reach the fuel-use goal.
The amount of diesel and LPG in DEA industry not being used by non-road machinery is included in the sectors, “Combustion in manufactur-ing industry” (0301) and “Non-industrial combustion plants” (0203) in the Danish emission inventory.
For recreational craft, the calculated fuel-use totals are subsequently sub-tracted from the DEA fishery (diesel) and road transport (gasoline) sec-tors.
<!�����The distinction between domestic and international emissions from avia-tion and navigation should be in accordance with the Revised 1996 IPCC
��������� The ratio between calculated fuel use and DEA fuel sales for 1990-2005
145
Guidelines for National Greenhouse Gas Inventories. For the national emission inventory, this, in principle, means that fuel sold (and associ-ated emissions) for flights/sea transportation starting from a sea-port/airport in the Kingdom of Denmark, with destinations inside or outside the Kingdom of Denmark, are regarded as domestic or interna-tional, respectively.
7�������For aviation, the emissions associated with flights inside the Kingdom of Denmark are counted as domestic. The flights from Denmark to Greenland and the Faroe Islands are classified as domestic flights in the inventory background data. In Greenland and in the Faroe Islands, the jet fuel sold is treated as domestic. This decision becomes reasonable when considering that almost no fuel is bunkered in Greenland/the Faroe Islands by flights other than those going to Denmark.
����(�����In DEA statistics, the domestic fuel total consists of fuel sold to Danish ferries and other ships sailing between two Danish ports. The DEA in-ternational fuel total consists of the fuel sold in Denmark to international ferries, international warships, other ships with foreign destinations, transport to Greenland and the Faroe Islands, tank vessels and foreign fishing boats.
In Greenland, all marine fuel sales are treated as domestic. In the Faroe Islands, the fuel sold in Faroese ports for Faroese fishing vessels and other Faroese ships is treated as domestic. The fuel sold to Faroese ships bunkering outside Faroese waters and the fuel sold to foreign ships in Faroese ports or outside Faroese waters is classified as international (Lastein and Winther, 2003).
To comply with the IPCC classification rules, the fuel used by vessels sailing to Greenland and the Faroe Islands should be a part of the do-mestic total. To improve the fuel data quality for Greenland and the Faroe Islands, the fuel sales should be grouped according to vessel desti-nation and IPCC classification, subsequently.
In conclusion, the domestic/international fuel split (and associated emis-sions) for navigation is not determined with the same degree of precision as for aviation. It is considered, however, that the potential of incorrectly allocated fuel quantities is only a small part of the total fuel sold for navigational purposes in the Kingdom of Denmark.
����,� $������������#�����������������������
Uncertainty estimates for greenhouse gases are made for road transport and other mobile sources using the guidelines formulated in the Good Practice Guidance and Uncertainty Management in National Greenhouse Gas Inventories (IPCC, 2000). For road transport, railways and a part of navigation (large vessels), these guidelines provide uncertainty factors for activity data that are used in the Danish situation. For other sectors, the factors reflect specific national knowledge (Winther et al., 2006). These sectors are (SNAP categories): Inland Waterways (a part of 1A3d: Navigation), Agriculture and Forestry (parts of 1A4c: Agricul-
146
ture/forestry/fisheries), Industry (mobile part of (1A2f: Industry-other) and Residential (1A4b).
The activity data uncertainty factor for civil aviation is based on own judgement.
The uncertainty estimates should be regarded as preliminary, only, and may be subject to changes in future inventory documentation. The calcu-lations are shown in Annex 3.B.15 for all emission components.
�� ������� Uncertainties for activity data, emission factors and total emissions in 2005 and as a trend
As regards time-series consistency, background flight data cannot be made available on a city-pair level prior to 2000. However, aided by LTO/aircraft statistics for these years and the use of proper assumptions, a sound level of consistency is, in any case, obtained for this part of the transport inventory.
The time-series of emissions for mobile machinery in the agriculture, for-estry, industry, household and gardening (residential) and inland wa-terways (part of navigation) sectors are less certain than time-series for other sectors, since DEA statistical figures do not explicitly provide fuel use information for working equipment and machinery.
������ =!� �������!����:>!� ��������� �1=7:='2�
The intention is to publish a sector report for road transport and other mobile sources annually. The last sector report prepared concerned the 2004 inventory.
The QA/QC descriptions of the Danish emission inventories for trans-port follow the general QA/QC description for NERI in Section 1.6, based on the prescriptions given in the IPCC Good Practice Guidance and Uncertainty Management in National Greenhouse Gas Inventories (IPCC, 2000).
An overview diagram of the Danish emission inventory system is pre-sented in Figure 1.2 (Data storage and processing levels), and the exact
Category Activity data CO2 CH4 N2O
% % % %
Road transport 2 5 40 50
Military 2 5 100 1000
Railways 2 5 100 1000
Navigation (small boats) 42 5 100 1000
Navigation (large vessels) 2 5 100 1000
Fisheries 2 5 100 1000
Agriculture 26 5 100 1000
Forestry 32 5 100 1000
Industry (mobile) 36 5 100 1000
Residential 36 5 100 1000
Civil aviation 10 5 100 1000
Overall uncertainty in 2005 5 35 65
Trend uncertainty 5 7 255
147
definitions of Critical Control Points (CCP) and Points of Measurements (PM) are given in Section 1.6. The status for the PMs relevant for the mo-bile sector are given in the following text and the result of this investiga-tion indicates a need for future QA/QC activities in order to fulfil the QA/QC requirements from the IPCC GPG.
2��������(����1���6�
The following external data sources are used in the mobile part of the Danish emission inventories for activity data and supplementary infor-mation:
• Danish Energy Authority: Official Danish energy statistics • Danish Road Directorate: Road traffic vehicle fleet and mileage data • Civil Aviation Agency of Denmark: Flight statistics • Non-road machinery: Information from statistical sources, research
organisations, different professional organisations and machinery manufacturers.
• Ferries (Statistics Denmark): Data for annual return trips for Danish ferry routes.
• Ferries (Danish Ferry Historical Society): Detailed technical and op-erational data for specific ferries.
• Danish Meteorological Institute (DMI): Temperature data • The National Motorcycle Association: 2-wheeler data The emission factors come from various sources: • Danish Energy Authority: CO2 emission factors and lower heating
values (all fuel types) • COPERT III: Road transport (N2O and NH3) • COPERT IV: Road transport (all exhaust components, except CO2,
SO2, N2O and NH3) • Danish State Railways: Diesel locomotives (NOx, VOC, CO and TSP) • EMEP/CORINAIR guidebook: Civil aviation and supplementary • Non road machinery: References given in NERI reports. • National sea transport and fisheries: TEMA2000 (NOx, VOC, CO and
TSP) Table 3.33 to follow contains Id, File/Directory/Report name, Descrip-tion, Reference and Contacts. As regards File/Directory/Report name, this field refers to a file name for Id when all external data (time-series for the existing inventory) are stored in one file. In other cases, a com-puter directory name is given when the external data used are stored in several files, e.g. each file contains one inventory years external data or each file contains time-series of external data for sub-categories of ma-chinery. A third situation occurs when the external data are published in publicly available reports; here the aim is to obtain electronic copies for internal archiving.
Data Storage level 1
3.Completeness DS.1.3.1 Documentation showing that all possible national data sources are included by setting down the reason-ing behind the selection of datasets.
148
�� ������� Overview table of external data for transport
1) File name; 2) Directory in the NERI data library structure; 3) Reports available on the internet
�
��������(��7!�������1���(������������2�The official Danish energy statistics are provided by the Danish Energy Authority (DEA) and are regarded as complete on a national level. For most transport sectors, the DEA subsector classifications fit the SNAP classifications used by NERI. For non-road machinery, this is however not the case, since DEA do not distinguish between mobile and station-ary fuel use in the subsectors relevant for non-road mobile fuel con-sumption.
Id no
File/-Directory/-Report name
Description Activity data or emission factor
Reference Contacts Data agreement
T1 Transport energy1
Dataset for all transport energy use
Activity data The Danish Energy Author-ity (DEA)
Anders B. Hansen & Peter Dal
Yes
T2 Fleet and mileage data1
Road transport fleet and mileage data
Activity data The Danish Road Director-ate
Bo Ekman Pending
T3 Flight statis-tics2
Data records for all flights
Activity data Civil Aviation Agency of Denmark
Henrik Gravesen In place
T4 Non road machinery2
Stock and opera-tional data for non-road machin-ery
Activity data Non road Documentation report
Morten Winther No
T5 Emissions from ships3
Data for ferry traffic
Activity data Statistics Denmark Sonja Merkelsen No
T6 Emissions from ships3
Technical and operational data for Danish ferries
Activity data Navigation emission docu-mentation report
Hans Otto Kristensen No
T7 Temperature data3
Monthly avg of daily max/min temperatures
Other data Danish Meteorological Institute
Danish Meteorological Institute
No
T8 Fleet and mileage data1
Stock data for mopeds and motorcycles
Activity data The National Motorcycle Association
Henrik Markamp No
T9 CO2 emission factors1
DEA CO2 emis-sion factors (all fuel types)
Emission factor
The Danish Energy Author-ity (DEA)
Anders B. Hansen & Peter Dal
No
T10 COPERT IV emission fac-tors3
Road transport emission factors
Emission factor
Laboratory of applied ther-modynamics Aristotle Uni-versity Thessaloniki
Leonidas Ntziachristos No
T11 Railways emission fac-tors1
Emission factors for diesel locomo-tives
Emission factor
Danish State Railways Rikke Næraa Yes
T12 EMEP/CORINAIR guide-book3
Emission factors for navigation, civil aviation and supplementary
Emission factor
European Environment Agency
European Environment Agency
No
T13 Non road emission fac-tors3
Emission factors for agriculture, forestry, industry and house-hold/gardening
Emission factor
Non road Documentation report
Morten Winther No
T14 Emissions from ships3
Emission factors for national sea transport and fisheries
Emission factor
Navigation emission docu-mentation report
Morten Winther No
149
Here, NERI calculates a bottom-up non-road fuel use estimate and for diesel (land based machinery only) and LPG, the residual fuel quantities are allocated to stationary consumption. For gasoline (land-based ma-chinery) the relevant fuel use quantities for the DEA are smaller than the NERI estimates, and the amount of fuel use missing is subtracted from the DEA road transport total to account for all fuel sold. For recreational craft, no specific DEA category exists and, in this case, the gasoline and diesel fuel use is taken from road transport and fisheries, respectively.
The NERI non-road fuel modifications, thus, give DEA-SNAP differ-ences for road transport and fisheries.
A special note must be made for the DEA civil aviation statistical figures. The domestic/international fuel use division derives from bottom-up fuel use calculations made by NERI.
��������#�������������Figures for fleet numbers and mileage data are provided by the Danish Road Directorate. Being a sector institution under the Ministry of Trans-port and Energy, it is a basic task for the Danish Road Directorate to pos-sess comprehensive information on Danish road traffic. The fleet figures are based on data from the Car Register, kept by Statistics Denmark and are, therefore, regarded as very precise. In some cases, stock data are split into vehicle subcategories (COPERT III format), based on expert judgement. Annual mileage information comes from the Danish Road Directorate’s own traffic measurement points, questionnaires and statis-tical methods subsequently used to disaggregate total traffic volumes into vehicle subcategories and ages.
'��� �7�������7(�����.��������The Civil Aviation Agency of Denmark (CAA-DK) monitors all aircraft movements in Danish airspace and, in this connection, possesses data re-cords for all take-offs and landings at Danish airports. The dataset from 2001 onwards, among others consisting of aircraft type and origin and destination airports for all flights leaving major Danish airports, are, therefore, regarded as very complete. For inventory years before 2001, the most accurate data contain CAA-DK total movements from major Danish airports and detailed aircraft type distributions for aircraft using Copenhagen Airport, provided by the airport itself.
������#���������1�������#���������� �#���2�A great deal of new stock and operational data for non road machinery was obtained in a research project carried out by Winther et al. (2006) for the 2004 inventory. The source for the agricultural machinery stock of tractors and harvesters is Statistics Denmark. Sales figures for tractors, harvesters and construction machinery, together with operational data and supplementary information, are obtained from The Association of Danish Agricultural Machinery Dealers. IFAG (The Association of Pro-ducers and Distributors of Fork Lifts in Denmark) provides fork-lift sale figures, whereas total stock numbers for gasoline equipment are ob-tained from machinery manufacturers with large Danish market shares, with figures validated through discussions with KVL. Stock information disaggregated into vessel types for recreational craft was obtained from the Danish Sailing Association. A certain part of the operational data
150
comes from previous Danish non-road research projects (Dansk Teknologisk Institut, 1992 and 1993; Bak et al., 2003).
No statistical register exists for non-road machinery types and this af-fects the accuracy of stock and operational data. For tractors and har-vesters, Statistics Denmark provide total stock data based on information from questionnaires and the registers of crop subsidy applications kept by the Ministry of Agriculture. In combination with new sales figures per engine size from The Association of Danish Agricultural Machinery Dealers, the best available stock data are obtained. In addition, using the sources for construction machinery and fork lift sale figures are regarded as the only realistic approach for consolidated stock information for these machinery types. Use of this source-type also applies in the case of machinery types (gasoline equipment, recreational craft) where data is even scarcer.
To support the 2005 inventory, new 2005 stock data for tractors, harvest-ers, fork lifts and construction machinery was obtained from the same sources as in Winther et al. (2006). For non-road machinery in general, it is, however, uncertain if data in such a level can be provided annually in the future.
%�������1 ����������������2�Statistics Denmark provides information of annual return trips for all Danish ferry routes from 1990 onwards. The data are based on monthly reports from passenger and ferry shipping companies in terms of trans-ported vehicles passengers and goods. Thus, the data from Statistics Denmark are regarded as complete. Most likely the data can be provided annually in the future.
%�������1�����%�����B�������� � ������)��% 2�No central registration of technical and operational data for Danish fer-ries and ferry routes is available from official statistics. However, one valuable reference to obtain data and facts about construction and opera-tion of Danish ferries, especially in the recent 20 - 30 years is the archives of Danish Ferry Historical Society. Pure technical data has not only been obtained from this society´s archives, but some of the knowledge has been obtained through the personal insight about ferries from some of the members of the society, which have been directly involved in the ferry bussines for example consultants, naval architecs, marine engi-neers, captains and superintendents. However, until recently no docu-mentation of the detailed DFS knowledge was established in terms of written reports or a central database system.
To make use of all the ferry specific data for the Danish inventories, DSF made a data documentation as a specific task of the research project car-ried out by Winther (2007b). Unless additional funding can be made available, the DFS data are not going to be updated for the inventory years 2006+.
�����A������ �(��� ������!���The monthly average max/min temperature for Denmark comes from DMI. This source is self explanatory in terms of meteorological data. Data are publicly available for each year on the internet.
151
��������� �A������� ��7����������Road transport: 2-wheeler stock information (The National Motorcycle Association). Given that no consistent national data are available for mopeds in terms of fleet numbers and distributions according to new sales per year, The National Motorcycle Association is considered to be the professional organisation, where most expert knowledge is available. The relevant annual information is given as personal communication, a method which can be repeated in the future.
��������(��7!�������1'6����������.��������#� �&�������(��� !��2�The CO2 emission factors and lower heating values (LHV) are fuel-specific constants. The country-specific values from the DEA are used for all inventory years.
'6;�����C�COPERT IV provides factors for fuel use and for all exhaust emission components which are included in the national inventory. For several reasons, COPERT IV is regarded as the most appropriate source of road traffic fuel use and emission factors. First of all, very few Danish emis-sion measurements exist, so data are too scarce to support emission cal-culations on a national level. Secondly, most of the fuel-use and emission information behind the COPERT model are derived from the European 5th framework research projects ARTEMIS and Particulates, and the formulation of fuel-use and emission factors for all single vehicle catego-ries has been made by a group of road traffic emission experts. A large degree of internal consistency is, therefore, achieved. Finally, the COPERT model is regularly updated with new experimental findings from European research programmes and, apart from updated fuel-use and emission factors, the use of COPERT IV by many European coun-tries ensures a large degree of cross-national consistency in reported emission results.
����� �������� &����Aggregated emission factors of NOx, VOC, CO and TSP for diesel loco-motives are provided annually by the Danish State Railways. Taking into account available time resources for subsector emission calculations, the use of data from Danish State Railways is sensible. This operator ac-counts for around 90% of all diesel fuel used by railway locomotives in Denmark and the remaining diesel fuel is used by various private rail-ways companies. Setting up contacts with the private transport operators is considered to be a rather time consuming experience taking time away from inventory work in areas of greater emission importance.
�A�;:'6���7���(!�#�"����Fuel-use and emission data from the EMEP/CORINAIR guidebook is the prime and basic source for the aviation and navigation part of the Danish emission inventories. For aviation, the guidebook contains the most comprehensive list of representative aircraft types available for city-pair fuel-use and emission calculations. The data have been evalu-ated specifically for detailed national inventory use by a group of experts representing civil aviation administration, air traffic management, emis-sion modellers and inventory workers.
In addition, the EMEP/CORINAIR guidebook is the source of non-exhaust TSP, PM10 and PM2.5 emission factors for road transport, and the
152
primary source of emission factors for some emission components – typically N2O, NH3, heavy metals and PAH – for other mobile sources.
������#���������1.!� �!����#���������.������2�The references for non-road machinery fuel-use and emission factors are listed in Winther et al. (2006). The fuel-use and emission data is regarded as the most comprehensive data collection on a European level, having been thoroughly evaluated by German emission measurement and non-road experts within the framework of a German non-road inventory pro-ject.
������ ���������������#�.��������Emission factors for NOX, VOC, CO and TSP are taken from the TEMA2000 model developed for the Ministry of Transport. To a large ex-tent the emission factors originate from the exhaust emission measure-ment programme carried out by Lloyd’s (1995). For NOX, additional im-formation of emission factors in a time series going back to 1949, and PM10 and PM2.5 fractions of total TSP was provided by the engine manu-facturer MAN B&W.
The experimental work by Lloyd’s is still regarded as the most compre-hensive measurement campaign with results publicly available. The ad-ditional NOX and PM10/PM2.5 information comes from the world’s largest ship engine manufacturer and data from this source is consistent with data from Lloyd’s. Consequently the data used in the Danish inventories for national sea transport is regarded as the best available for emission calculations.
The uncertainty involved in the DEA fuel-use information (except civil aviation) and the CAA-DK flight statistics is negligible, as such, and this is also true for DMI temperature data. For civil aviation, some uncer-tainty prevails, since the domestic fuel-use figures originate from a divi-sion of total jet-fuel sales figures into domestic and international fuel quantities, derived from bottom-up calculations. A part of the fuel-use uncertainties for non-road machines is due to the varying levels of stock and operational data uncertainties, as explained in DS 1.3.1. The road transport fleet totals from the Danish Road Directorate and The National Motorcycle Association in the main vehicle categories are accurate. Un-certainties, however, are introduced when the stock data are split into vehicle subcategories. The mileage figures from the Danish Road Direc-torate are generally less certain and uncertainties tend to increase for disaggregated mileage figures on subcategory levels.
As regards emission factors, the CO2 factors (and LHVs) from the DEA are considered to be very precise, since they relate only to fuel. For the remaining emission factor sources, the SO2 (based on fuel sulphur con-tent), NOx, NMVOC, CH4, CO, TSP, PM10 and PM2.5 emission factors are less accurate. Though many measurements have been made, the experi-mental data rely on the individual measurement and combustion condi-tions. The uncertainties for N2O and NH3 emission factors increase even further due to the small number of measurements available. For heavy
Data Storage level 1
1. Accuracy DS.1.1.1 General level of uncertainty for every dataset, including the reasoning for the specific values
153
metals and PAH, experimental data are so scarce that uncertainty be-comes very high.
A special note, however, must be made for energy. The uncertainties due to the subsequent treatment of DEA data for road transport, fisheries and the non-road relevant sectors, explained in DS 1.3.1, trigger some uncer-tainties in the fuel-use figures for these sectors. This point is, though, more relevant for QA/QC description for data processing, Level 1.
The general uncertainties of the DEA fuel-use information, DMI tem-perature data, road transport stock totals and the CAA-DK flight statis-tics are zero. For domestic aviation fuel use, the uncertainty is as pre-scribed by the IPCC Good Practice Guidance manual. For road transport, it is not possible to quantify the uncertainties (1) of stock distribution into COPERT IV-relevant vehicle subsectors and (2) of the national mile-age figures, as such. For non-road machinery stock and operational data, the uncertainty figures are given in Winther et al. (2006).
For emission factors, the uncertainties for mobile sources are determined as suggested in the IPCC and UNECE guidelines. The uncertainty fig-ures are listed in Paragraph 3.1.3 for greenhouse gases, and in Illerup et al. (2005b) and Winther et al. (2006) for the remaining emission compo-nents.
Work has been carried out to compare Danish figures with correspond-ing data from other countries in order to evaluate discrepancies. The comparisons have been made on a CRF level, mostly for implied emis-sion factors (Thomsen et al., 2006).
It is ensured that the original files from external data sources are ar-chived internally at NERI. Subsequent raw data processing is carried out either in the NERI database models or in spreadsheets (data processing level 1).
For transport, NERI has made formal agreements with regard to external data deliverance with (Table 3.32 external data source Id’s in brackets): DEA (T1), CAA-DK (T3), Danish State Railways (T9) and the Danish Road Directorate (T2). The latter agreement is currently being renegoti-
Data Storage level 1
1. Accuracy DS.1.1.2 Quantification of the uncertainty level of every single data value including the reasoning for the specific values.
Data Storage level 1
2.Comparability DS.1.2.1 Comparability of the data values with similar data from other countries, which are comparable with Denmark, and evaluation of discrepancy.
Data Storage level 1
4.Consistency DS.1.4.1 The origin of external data has to be preserved whenever possible without explicit arguments (referring to other PMs)
Data Storage level 1
6.Robustness DS.1.6.1 Explicit agreements between the exter-nal institution holding the data and NERI about the condition of delivery
154
ated due to an internal restructuring of the traffic data responsibilities in the Ministry of Transport and Energy.
Please refer to DS 1.1.1. In this measurement point, the reason for exter-nal data selections in different inventory areas is given.
The references for external datasets are provided in the present report.
The following list shows the external data source (source Id in brackets), the responsible person and contact information for each area where for-mal data deliverance agreements have been made.
In the mobile part of the Danish emission inventories, no uncertainty as-sessments are made at Data Processing Level 1, except for non-road ma-chinery and recreational craft. For these types of mobile machinery, the stock and operational data variations are assumed to be normally dis-tributed (Winther et al., 2006). Tier 1 uncertainty calculations produce fi-nal fuel-use uncertainties ready for Data Storage Level 2 (SNAP level 2: Inland waterways, agriculture, forestry, industry and household-gardening).
For non-road machinery and recreational craft, uncertainty assessments are made by Winther et al. (2006), and the sizes of the variation intervals are given for activity data and emission factors.
Data Storage level 1
7.Transparency DS.1.7.1 Summary of each dataset, including the reasoning for selecting the specific dataset
Data Storage level 1
7.Transparency DS.1.7.3 References for citation for any external dataset have to be available for any single value in any dataset.
Data Storage
level 1
7.Transparency DS.1.7.4 Listing of external contacts for every dataset
Data Processing level 1
1. Accuracy DP.1.1.1 Uncertainty assessment for every data source as input to Data Storage level 2 in relation to type of variability. (Distri-bution as: normal, log normal or other type of variability)
Data Processing level 1
1. Accuracy DP.1.1.2 Uncertainty assessment for every data source as input to Data Storage level 2 in relation to scale of variability (size of variation intervals)
155
An evaluation of the methodological inventory approach has been made, which proves that the emission inventories for transport are made ac-cording to the international guidelines (Winther, 2005: Kyoto notat, in Danish). This paper will be translated into English and the conclusions will be implemented in the future national inventory reports.
It has been checked that the greenhouse gas emission factors used in the Danish inventory are within margin of the IPCC guideline values.
See DP 1.1.3.
The most important area where the accessibility to critical data is lacking is road transport. More accurate national vehicle fleet and mileage data is available from the Danish Vehicle Inspection Programme, and new fuel use and emission information is available in a new version of COPERT- COPERT IV. It is, however, not straight forward to combine the new traffic and emission data, due to different formats. Instead the new data are transformed into COPERT III input formats. using different assumptions. Work will be made this year by the Ministry of Transport and Energy to transform the new fleet and mileage traffic data into COPERT IV format.
A log will be incorporated in the NERI transport models, explaining the model changes (input data, model principles), whenever they occur. The current explanations are included in Chapter 3.3 of the present report.
During model development it has been checked that all mathematical model relations give exactly the same results as independent calcula-
Data Processing level 1
1. Accuracy DP.1.1.3 Evaluation of the methodological ap-proach using international guidelines
Data Processing level 1
1. Accuracy DP.1.1.4 Verification of calculation results using guideline values
Data Processing level 1
2.Comparability DP.1.2.1 The inventory calculation has to follow the international guidelines suggested by UNFCCC and IPCC.
Data Processing level 1
3.Completeness DP.1.3.1 Assessment of the most important quantitative knowledge which is lack-ing.
Data Processing level 1
3.Completeness DP.1.3.2 Assessment of the most important cases where access is lacking with regard to critical data sources that could improve quantitative knowledge.
Data Processing level 1
4.Consistency DP.1.4.1 In order to keep consistency at a high level, an explicit description of the activities needs to accompany any change in the calculation procedure.
Data Processing level 1
5.Correctness DP.1.5.1 Show at least once, by independent calculation, the correctness of every data manipulation.
156
tions. A list of examples with model and independent calculation results, one set for each mathematical model expression, will be made.
When NERI transport model changes are made relating to fuel use, it is checked that the calculated fuel-use sums correspond to the expected fuel-use levels in the time-series. The fuel-use check also includes a time-series comparison with fuel-use totals calculated in the previous model version. The checks are performed on a SNAP level and, if appropriate, detailed checks are made for vehicle/machinery technology splits.
As regards model changes in relation to derived emission factors (and calculated emissions), the time-series of emission factors (and emissions) are compared to previous model figures. A part of this evaluation in-cludes an assessment, if the development corresponds to the underlying assumptions given by detailed input parameters. Among other things, the latter parameters depend on emission legislation, new technology phase-in, deterioration factors, engine operational conditions/driving modes, gasoline evaporation (hydrocarbons) and cold starts. For metho-dological issues, please refer to Section 3.3.2.
For road transport, aviation and non-road machinery, whether all exter-nal data are correctly put into the NERI transport models is checked. This is facilitated by the use of sum queries which sum up stock data (and mileages for road transport) to input aggregation levels. However, spreadsheet or database manipulations of external data are, in some cases, included in a step prior to this check.
This is carried out in order to produce homogenous input tables for the NERI transport models (road, civil aviation, non-road machin-ery/recreational craft, navigation/fisheries). The sub-routines perform operations, such as the aggregation/disaggregation of data into first sales year (Examples: Fleet numbers and mileage for road transport, stock numbers for tractors, harvesters, fork lifts) or simple lists of total stock per year (per machinery type for e.g. household equipment and for recreational craft). For civil aviation, additional databases control the al-location of representative aircraft to real aircraft types and the cruise dis-tance between airports. A more formal description of the sub-routines will be made.
Regarding fuel data, it is checked for road transport and civil aviation that DEA totals (modified for road) match the input values in the NERI models. For the transport modes military, railways, national sea trans-port and fisheries, the DEA fuel-use figures go directly into Data Storage Level 2. This is also the case for the railway emission factors obtained
Data Processing level 1
5.Correctness DP.1.5.2 Verification of calculation results using time-series
Data Processing level 1
5.Correctness DP.1.5.3 Verification of calculation results using other measures
Data Processing
level 1
5.Correctness DP.1.5.4 Show one-to-one correctness between external data sources and the data bases at Data Storage level 2
157
from Danish State Railways and, generally, for the emission factors which are kept constant over the years.
The NERI model simulations of fuel-use and emission factors for road transport, civil aviation and non-road machinery refer to Data Processing Level 1.
The NERI model calculation principles and basic equations are thor-oughly described in the present report, together with the theoretical model reasoning and assumptions. Documentation is also given e.g. in Illerup et al. (2005b), Winther (2001, 2007a, 2007b) and Winther et al. (2006).
In the different documentation reports for transport in the Danish emis-sion inventories, there are explicit references for the different external data used.
Recalculation changes in the emission inventories are described in the NIR and ECE reports as a standard. A manual log table in the NERI transport models to collect information about recalculations based on changes in emission factors and/or activity data will be established.
2���� ���(����1���8�
In the various documentation reports behind the transport part of the Danish emission inventories there is a thorough documentation of the SNAP aggregated fuel use figures and emission factors, based on the original external data derived from external sources.
At present, a NERI software programme imports data from prepared in-put data tables (SNAP fuel-use figures and emission factors) into the CollectER database.
Tables for CollectER fuel use and emission results are prepared by a spe-cial NERI database (NERIrep.mdb). The results relevant for mobile sources are copied into a database containing all the official inventory re-sults for mobile sources (Data2005 NIR-UNECE.mdb). By the use of da-tabase queries, the results from this latter database are aggregated into
Data Processing level 1
7.Transparency DP.1.7.1 The calculation principle and equations used must be described
Data Processing level 1
7.Transparency DP.1.7.2 The theoretical reasoning for all meth-ods must be described
Data Processing level 1
7.Transparency DP.1.7.3 Explicit listing of assumptions behind all methods
Data Processing
level 1
7.Transparency DP.1.7.4 Clear reference to dataset at Data Storage level 1.
Data Processing
level 1
7.Transparency DP.1.7.5 A manual log to collect information about recalculations
Data Storage
level 2
5.Correctness DS.2.5.1 Documentation of a correct connection between all data types at level 2 to data at level 1
Data Storage level 2
5.Correctness DS.2.5.2 Check if a correct data import to level 2 has been made.
158
the same formats as being used by the relevant NERI transport models in their results calculation part. The final comparison between CollectER and NERI transport model results are set up in a spreadsheet.
!((����#�=7:='�� ��.�����"� ����!�����The following points make up the list of QA/QC tasks to be carried out directly in relation to the mobile part of the Danish emission inventories. The time plan for the individual tasks has not yet been prepared.
2��������(����1���6�
• Storage of external data (temperature distribution), EMEP-CORINAIR guidebook (mobile chapters).
• An elaboration of the PAH and heavy metal part of the inventory for mobile sources. Review of existing emission factors and inclusion of new sources.
• Finalisation of the data deliverance agreement for road transport. 2������������(���1���6�
• A log in the NERI transport models explaining model changes (input data, model principles)
• Inclusion of new Danish mileage data (source: Ministry of Transport and Energy)
• Inclusion of updated emission factors for road transport (source: AR-TEMIS and Particulates)
• Documentation list of model and independent calculations to test every single mathematical relation in the NERI transport models
• A formal description of sub-routines for external data manipulation 2��������(����1���8�
• Development of a model that can check the correct data transfer from input tables to CollectER.
���� �! ������The following recalculations and improvements of the emission invento-ries have been made since the emission reporting in 2005.
���#����������A revision of the 1985-2004 time-series of emissions has been made, based on revised mileage data from the Danish Road Directorate (de-rived from the Danish vehicle inspection and maintenance programme) and updated emission factors from the latest version of the European road transport emission model - COPERT IV.
A� ������A revision of the 1985-2004 time-series of emission factors has been made based on new aggregated emission factors from road transport.
��� &����No changes have been made.
� �#�&����&����A fuel use error has been corrected for the years 1985-2003, giving fuel use and emission decreases for this time period. This affects also the fishery category, since more fuel now appears in fisheries.
159
������ ���������������#�.��������A new research project carried out by Winther (2007b) has given new knowledge, and the following changes have therefore been made to the national inventory: 1) Updated emission factors has given some changes in the total emissions from 1985-2004, 2) The residual fuel use amount from the fishery sector in the national energy statistics, has been moved to the national sea transport category, resulting in fuel use and emission changes 1985-2004.
Less diesel fuel is subtracted from fisheries, due to an error correction for inland waterways. This results in fuel use and emission changes 1985-2003.
7�������No changes have been made.
7(���! �!���Updated stock information for tractors and harvesters 2001-2004, has given a fuel use and emissions increase for these years.
$�����������The uncertainty factors for activity data have been changed to reflect specific national knowledge ((Winther et al., 2006) in the following SNAP categories: Inland waterways (a part of 1A3d: Navigation), Agriculture and Forestry (parts of 1A4c: Agriculture/forestry/fisheries), Industry (mobile part of 1A2f: Industry-other) and Residential (1A4b).
; ��#�������������The ongoing aspiration is to fulfil the requirements from UNECE and UNFCCC for good practice in inventory preparation for transport. A study has been completed for transport, reviewing the different issues of choices relating to methods (methods used, emission factors, activity data, completeness, time-series consistency, uncertainty assessment) re-porting and documentation, and inventory quality assurance/quality control. This work and the overall priorities of NERI, taking into account emission source importance (from the Danish 2004 key source analysis), background data available and time resources, lay down the following list of improvements to be made in future.
��������.�������The Danish greenhouse gas emission factors will be compared with the factors suggested by IPCC.
=7:='� �Future improvements regarding this issue are dealt with in Section 3.1.4.
��.�������.���'����������
Bak, F., Jensen, M.G., Hansen, K.F. 2003: Forurening fra traktorer og ik-ke-vejgående maskiner i Danmark, Miljøprojekt nr. 779, Danish EPA, Copenhagen (in Danish).
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Cappelen, J., Jørgensen, B.V. 2006: The Climate of Denmark 2005, with Thorshavn, Faroe Islands and Nuuk, Greenland - with English transla-tions, Tecnical report No 06-01, pp. 48, Danish Meteorological Institute.
Cappelen, J., Jørgensen, B.V. 2005: The Climate of Denmark 2004 with the Faroe Islands and Greenland - with Danish translations, Tecnical re-port No 05-01, pp. 88, Danish Meteorological Institute.
Cappelen, J. 2004: The Climate of Denmark - Key climatic Figures 2000-2003, Tecnical report No 04-05, pp 23, Danish Meteorological Institute.
Cappelen, J. 2003: The Climate of Denmark - Key climatic Figures 1980-1989, Tecnical report No 03-15, pp 47, Danish Meteorological Institute.
Cappelen, J. 2000: The Climate of Denmark - Key climatic Figures 1990-1999, Tecnical report No 00-08, pp 47, Danish Meteorological Institute.
Danish Energy Authority, 2006: The Danish energy statistics, Available at http://www.ens.dk/graphics/UK_Facts_Figures/Statistics/yearly_s-tatistics/BasicData2005.xls (15-04-2007).
Dansk Teknologisk Institut, 1992: Emission fra Landbrugsmaskiner og Entreprenørmateriel, commissioned by the Danish EPA and made by Miljøsamarbejdet in Århus (in Danish).
Dansk Teknologisk Institut, 1993: Emission fra Motordrevne Arbejdsred-skaber og –maskiner, commissioned by the Danish EPA and made by Miljøsamarbejdet in Århus (in Danish).
Ekman, B. 2005: Unpublished data material from the Danish Road Direc-torate.
EMEP/CORINAIR, 2004: Emission Inventory Guidebook 3rd edition, prepared by the UNECE/EMEP Task Force on Emissions Inventories and Projections, 2004 update. Available at http://reports.eea.eu.int/EM-EPCORINAIR4/en (15-04-2007)
ICAO Annex 16: "International standards and recommended practices", Volume II "Aircraft Engine Emissions", 2th ed. (1993), plus amendments: Amendment 3 20th March 1997 and amendment 4 4 November 1999.
IFEU 2004: Entwicklung eines Modells zur Berechnung der Luftschadstoffemissionen und des Kraftstoffverbrauchs von Verbren-nungsmotoren in mobilen Geräten und Maschinen - Endbericht, UFOPLAN Nr. 299 45 113, pp. 122, Heidelberg.
Illerup, J.B., Birr-Pedersen, K., Mikkelsen, M.H., Winther, M., Gyldenkærne, S., Bruun, H.G. & Fenhann, J. 2002: Projection Models 2010. Danish emissions of SO2, NOX, NMVOC and NH3. National Envi-ronmental Research Institute, Denmark. 192 pg - NERI Technical Report No. 414.
Illerup, J.B., Lyck, E., Nielsen, M., Winther, M., Mikkelsen, M.H., Hoff-mann, L., Sørensen, P.B., Vesterdal, L. & Fauser, P. 2005: Denmark’s Na-tional Inventory Report - Submitted under the United Nations Frame-
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work Convention on Climate Change, 1990-2002. Emission inventories. National Environmental Research Institute, Denmark. 1099 pp. – Re-search Notes from NERI no. 196. http://research-notes.dmu.dk
Illerup, J.B., Lyck, E., Nielsen, M., Winther, M., Hoffmann, L., & Mikkel-sen, M.H. 2005: Annual Danish Emission Inventory Report to UNECE. Inventories from the base year of the protocols to year 2002. National Environmental Research Institute, Denmark. Research Notes from NERI (to be published).
IPCC, 2000: Good Practice Guidance and Uncertainty Management in National Greenhouse Gas Inventories. Available at http://www.ipcc-nggip.iges.or.jp/public/gp/english/ (15-04-2007).
Lastein, L. & Winther, M. 2003: Emission of greenhouse gases and long-range transboundary air pollutants in the Faroe Islands 1990-2001. Na-tional Environmental Research Institute. - NERI Technical Report 477: 62 pp. (electronic). Available at: http://www.dmu.dk/1_viden/2_Publi-kationer/3_fagrapporter/FR477.PDF
Markamp 2006: Personal communication, Henrik Markamp, The Na-tional Motorcycle Association.
Marpol 73/78 Annex VI: Regulations for the prevention of air pollution from ships, technical and operational implications, DNV, 21 February 2005.
Ministry of Transport 2000: TEMA2000 - et værktøj til at beregne trans-porters energiforbrug og emissioner i Danmark (TEMA2000 - a calcula-tion tool for transport related fuel use and emissions in Denmark). Tech-nical report. Available at (http://www.trm.dk/sw664.asp).
Ntziachristos, L. & Samaras, Z. 2000: COPERT III Computer Programme to Calculate Emissions from Road Transport - Methodology and Emis-sion Factors (Version 2.1). Technical report No 49. European Environ-ment Agency, November 2000, Copenhagen. Available at: http://reports.eea.eu.int/Technical_report_No_49/en (June 13, 2003).
Næraa, R. 2006: Unpublished data material from the Danish State Rail-ways.
Nørgaard, T., Hansen, K.F. 2004: Chiptuning af køretøjer - miljømæssig effekt, Miljøprojekt nr. 888, Miljøstyrelsen.
Pulles, T., Aardenne J.v., Tooly, L. & Rypdal, K. 2001: Good Practice Guidance for CLRTAP Emission Inventories, Draft chapter for the UN-ECE CORINAIR Guidebook, 7 November 2001, 42pp.
Sørensen, P.B., Illerup, J.B., Nielsen, M., Lyck, E., Bruun, H.G., Winther, M., Mikkelsen, M.H. & Gyldenkærne, S. 2005: Quality manual for the green house gas inventory. Version 1. National Environmental Research Institute. - Research Notes from NERI 224: 25 pp. (electronic). Available at: http://www2.dmu.dk/1_viden/2_Publikationer/3_arbrapporter/A-R224.pdf
162
Thomsen, M., Fauser, P. 2006: Verification of the Danish emission inven-tory data by national and international data comparisons. NERI working report. To be published.
USEPA 2004: Conversion Factors for Hydrocarbon Emission Compo-nents. EPA420-P-04-001, US Environmental Protection Agency, 5 pp.
Winther, M. 2001a: 1998 Fuel Use and Emissions for Danish IFR Flights. Environmental Project no. 628, 2001. 112 p. Danish EPA. Prepared by the National Environmental Research Institute, Denmark. Available at http://www.mst.dk/udgiv/Publications/2001/87-7944-661-2/html/.
Winther, M. 2001b: Improving fuel statistics for Danish aviation. Na-tional Environmental Research Institute, Denmark. 56 p. – NERI Techni-cal Report No. 387.
Winther, M. 2005: Kyoto notat - Transport. Internal NERI note (unpub-lished). 4 p. (in Danish).
Winther, M. & Nielsen, O.K. 2006: Fuel use and emissions from non-road machinery in Denmark from 1985–2004 – and projections from 2005-2030. The Danish Environmental Protection Agency. - Environmental Project 1092: 238 pp. Available at: http://www.dmu.dk/Udgivelser/-Arbejdsrapporter/Nr.+200-249/
Winther, M. 2007a: Danish emission inventories for road transport and other mobile sources. Inventories until year 2004. National Environ-mental Research Institute. - Research Notes from NERI 236: 244 pp. (elec-tronic). Available at: http://www2.dmu.dk/1_viden/2_Publikationer/-3_arbrapporter/rapporter/AR201.pdf
Winther, M. 2007b: Fuel use and emissions from navigation in Denmark from 1985-2005 - and projections from 2006-2030. - Research notes. The Danish Environmental Protection Agency (in press).
In addition to the sector-specific CO2 emission inventories (the national approach), the CO2 emission is also estimated using the reference ap-proach described in the IPCC Reference Manual (IPCC, 1997). The refer-ence approach is based on data for fuel production, import, export and stock change. The CO2 emission inventory based on the reference ap-proach is reported to the Climate Convention and used for verification of the official data in the national approach.
Data for import, export and stock change used in the reference approach originate from the annual “basic data” table prepared by the Danish En-ergy Authority and published on their home page (Danish Energy Au-thority, 2006b). The fraction of carbon oxidised has been assumed to be 1.00. The carbon emission factors are default factors originating from the IPCC Reference Manual (IPCC, 1997). The country-specific emission fac-
163
tors are not used in the reference approach, the approach being for the purposes of verification.
The Climate Convention reporting tables include a comparison of the na-tional approach and the reference approach estimates. To make results comparable, the CO2 emission from incineration of the plastic content of municipal waste is added in the reference approach while the fuel con-sumption is subtracted.
Three fuels are used for non-energy purposes: lube oil, bitumen and white spirit. The total consumption for non-energy purposes is relatively low – 12.0 PJ in 2005.
In 2005 the fuel consumption rates in the two approaches differ by -1.27% and the CO2 emission differs by -1.15%. In the period 1990-2005 both the fuel consumption and the CO2 emission differ by less than 1.6%. The differences are below 1% for all years except 1998 and 2005. Accord-ing to IPCC Good Practice Guidance (IPCC, 2000) the difference should be within 2%. A comparison of the national approach and the reference approach is illustrated in Figure 3.61.
���������� Comparison of the reference approach and the national approach.
��,� %!(���������������1'�%��������*<2�
��,�*� �!��������(����#����������
%!(��������������.������ �#�.!� �)�'�%��������*<*��Coal mining is not occurring in Denmark and no emissions are estimated for solid fuel.
%!(���������������.������ �1*<��2�The category “Fugitive emissions from oil (1B2a)” includes emissions from offshore activities and refineries.
%!(���������������.������!�� �(��)�������������#�#�����"!����1'�%��������*<�"2�In the year 2005, the length of transmission pipelines excluding offshore pipeline is 860 km. The length of distribution pipelines was 18682 km in
-2,00
-1,50
-1,00
-0,50
0,00
0,50
1,00
1,50
2,00
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
%
Difference Energy consumption [%] Difference CO2 emission [%]
164
2005 (cast iron 0 km, steel 1868 km, plastics 16814 km). Two natural gas storage facilities are in operation in Denmark. In 2005 the gas input was 530 Mm3 and the withdrawal was 555 Mm3. Emission from gas storage is included in transmission.
% ���()�(���1'�%��������*<��)�% ���(���2�Offshore flaring of natural gas is the main source of emissions in the Fu-gitive emission sector. Flaring in gas treatment and gas storage plants are, however, also included in the sector.
��,��� A���#� �(��� ����!����
%!(���������������.������ �1*<��2�6..����������������Emissions from offshore activities include emissions from extraction of oil and gas, onshore oil tanks, onshore and offshore loading of ships.
The total emission can then be expressed as:
��������������� ��� ���� tan++=
������)���+� ��� ����+��.���������
According to the EMEP/CORINAIR Guidebook (EMEP/CORINAIR, 2004) the total fugitive emissions of VOC from extraction can be esti-mated by means of Equation 3.5.2.
where NP is the number of platforms, Pgas (106 Nm3) is the production of gas and Poil (106 tons) is the production of oil.
It is assumed that the VOC contains 75% methane and 25% NMVOC, meaning that the total emission of CH4 and NMVOC for extraction of oil and gas can be calculated as:
����������� �����
���������������������������������
�������
���
,62
,,,
)105.8101.12.40(25.0 ⋅+⋅⋅+⋅+⋅=
+=−−
4,62
4,4;4,
)105.8101.12.40(75.0 ��������������
��������������� ������� ��� ��
�������
���
⋅+⋅⋅+⋅+⋅=
+=−−
In Denmark, the venting of gas is assumed to be negligible because con-trolled venting enters the gas flare system.
������This source includes the transfer of oil from storage tanks or directly from the well into a ship. This activity also includes losses during trans-port. When oil is loaded hydrocarbon vapour will be displaced by oil
(3.5.1)
(3.5.2)
(3.5.5)
(3.5.3)
(3.5.4)
165
and new vapour will be formed, both leading to emissions. The emis-sions from ships are calculated by equation 3.5.5.
������������� ����� ⋅=
where EMFships is the emission factor for loading of ships off-shore and on-shore and Loil is the amount of oil loaded.
����������The emissions from storage of raw oil are calculated by equation 3.5.6.
������������ ⋅= tantan
where EMFtanks is the emission factor for storage of raw oil in tanks.
��� ��!������Activity data used in the calculations of the emissions is shown in Table 3.33 and is based on information from the Danish Energy Authority (Danish Energy Authority, 2006a and 2006b) or from the green accounts from the Danish gas transmission company DONG (DONG, 2006).
���������� Activity data for 2005
Mass weight raw oil = 0.86 ton/m3
In the EMEP/CORINAIR Guidebook (EMEP/CORINAIR, 2004) emis-sion factors for different countries are given. In the Danish emission in-ventory the Norwegian emission factors are used (EMEP/CORINAIR, 2004, Table 3.30). The emissions for storage of oil are given in the green accounts from DONG for 2005 (DONG, 2006) and the emission factor is calculated on the basis of the amount of oil transported in pipeline.
���������� Emission factors.
From the activity data in Table 3.33 and the emission factors in Table 3.34 the emissions for NMVOC and CH4 are calculated in Table 3.35.
Activity Symbols Year
2005 Ref.
Number of platforms Np 50 Danish Energy Agency (2006a)
Produced gas (106Nm3) Pgas 11523 Danish Energy Agency (2006a)
Produced oil(103m3) Poil,vol 21886 Danish Energy Agency (2006a)
Produced oil (103ton) Poil 18822 Danish Energy Agency (2006a)
Oil loaded (103m3) Loil off-shore 3880 Danish Energy Agency (2006a)
Oil loaded (103ton) Loil off-shore 3337 Danish Energy Agency (2006a)
Ships off-shore 0.00005 0.001 Fraction of loaded EMEP/CORINAIR, 2004
Ships on-shore 0.000002 0.0002 Fraction of loaded EMEP/CORINAIR, 2004
Oil tanks 113 249 kg/103m3 DONG, 2006
(3.5.6)
166
��������� CH4 emissions for 2005 (tonnes)
CH4 NMVOC
Extraction (fugitive) 1594 531
Oil tanks 2045 4507
Offshore loading of ships 167 3337
Onshore loading of ships 25 2494
Total 3831 10869
6� ���.�������Petroleum products processing: in the production process at refineries, a part of the volatile hydrocarbons (VOC) is emitted to the atmosphere. It is assumed that CH4 accounts for 1% and NMVOC for 99% of the emis-sions. The VOC emissions from the petroleum refinery processes cover non-combustion emissions from feedstock handling/storage, petroleum products processing, product storage/handling and flaring. SO2 is also emitted from the non-combustion processes and includes emissions from products processing and sulphur recovery plants. The emission calcula-tions are based on information from the Danish refineries and the energy statistics.
��������� Oil Refineries. Processed crude oil, emissions and emission factors
%!(���������������.������!�� �(��)�������������#�#�����"!����1'�%��������*<�"2��Inventories of CH4 emission from gas transmission and distribution are based on annual environmental reports from DONG and on a Danish emission inventory for the years 1999-2005 reported by the Danish gas sector (transmission and distribution companies) (Karll 2003, Karll 2005 & Oertenblad 2006). The inventories estimated by the Danish gas sector are based on the work carried out by Marcogas and the International Gas Union (IGU).
In the 1990-1999 inventories, fugitive CH4 emissions from storage facili-ties and the gas treatment plant are included in the emission factor for transmission. In the 2000-2005, emission inventories transmission, gas storage and gas treatment are registered separately and added.
Gas transmission data are shown in Table 3.37. Emissions from gas stor-age facilities and venting in the gas treatment plant are shown in Table 3.38. Gas distribution data are shown in Table 3.39.
���������� CH4 emission from natural gas transmission
1) In 1990-1997 transmission rates refer to Danish energy statistics, in 1998 the transmission rate refers to the annual environmental report of DONG, in 1999-2005 emissions refer to DONG/Danish Gas Technology Centre (Karll 2003, Karll 2005, Oertenblad 2006)
2) In 1991-95 CH4 emissions are based on the annual environmental report from DONG for the year 1995. In 1996-99 the CH4 emission refers to the annual environmental reports from DONG for the years 1996-99. In 2000-2005 the CH4 emission refers to DONG/Danish Gas Technology Centre (Karll 2003, Karll 2005, Oertenblad 2006)
3) IEF=Emission/transmission_rate. In 1990 the IEF is assumed to be the same as in 1991.
���������� Additional fugitive CH4 emissions from natural gas storage facilities and venting in gas treat-ment plant (Mg)
��������� CH4 emission from natural gas distribution
1) In 1999-2005 distribution rates refer to DONG / Danish Gas Technology Centre / Danish gas distribution companies (Karll 2005, Oertenblad 2006), In 1990-98 distribution rates are estimated from the Danish energy statistics. Distribution rates are assumed to equal total Danish consumption rate minus the consumption rates of sectors that receive the gas at high pressure. The following consumers are assumed to receive high pressure gas: town gas production companies, production platforms and power plants.
2) Danish Gas Technology Centre / DONG/ Danish gas distribution companies (Karll 2003)
3) In the years 1999-2005 IEF=CH4 emission / distribution rate. In 1990-1998 an average of the IEF in 1999-2001 is assumed.
4) Data from Naturgas Fyn not included (data not complete)
5) Assumed same emission as in 2002
The methane emission from the Danish gas distribution system is meas-ured and calculated in accordance with the scheme prepared by the in-ternational working group, Marcogaz, realising the particular character-istics of the Danish distribution system.
The methane emission factor is found to be significantly lower in Den-mark than in any other European country; the reason being that the dis-tribution system in Denmark is relatively new.
In contrast to other countries with old distribution systems, partially made of cast ion pipes, the Danish Polyethylene (PE) distribution system is basically tight with minimal fugitive losses. The PE pipes, however, are vulnerable. Therefore, the methane emission in Denmark is largely caused by excavation damages, but emissions also occur in connection with construction and maintenance activities performed by the gas com-panies. These losses are measured or estimated by calculation in each case.
The Danish emission figures are produced by the individual gas compa-nies and are collected, reviewed and reported by the Danish Gas Tech-nology Centre (Karll, 2006).
According to the environmental report of Nybro gas treatment plant de-sulphurisation of the natural gas produced in Denmark takes place off shore. Therefore, usually no desulphurisation takes place in the gas treatment plant Nybro. However, in 2004, the desulphurisation plant op-erated for a total of 30 hours. So far the inventories has not included off shore desulphurisation. This might be relevant for future inventories.
% ���()�(���1'�%��������*<��)�% ���(���2�Emissions from offshore flaring are estimated based on data for fuel con-sumption from the Danish energy statistics (DEA, 2006b) and emission factors for flaring. The emissions from flaring in gas treatment and gas storage plants are estimated based on the annual environmental reports of the plants.
The fuel consumption rates are shown in Table 3.40. Flaring rates in gas treatment and gas storage plants are not available until 1995.
The emission factors for offshore flaring are shown in Table 3.41. The CO2 emission factor follows the same time-series as natural gas com-busted in stationary combustion plants. All other emission factors are constant in 1990-2005.
The time-series for the CO2 emission from gas flaring fluctuates due to the fluctuation of offshore flaring rates as shown in Figure 3.61.
���������� Natural gas flaring rate (DEA 2006b)
Year Flaring, offshore [TJ] Gas treatment and gas storage [TJ]
1990 4218 -
1991 8692 -
1992 8977 -
1993 7819 -
1994 7709 -
1995 5964 43
1996 6595 30
1997 9629 35
1998 7053 29
1999 15509 32
2000 10023 29
2001 10806 36
2002 8901 44
2003 9333 33
2004 10299 25
2005 7269 42
169
���������� Emission factors for offshore flaring of natural gas
Pollutant Emission factor
CO2 56,96 kg/GJ
CH4 5 g/GJ
N2O 1 g/GJ
SO2 0,3 g/GJ
NOx 300 g/GJ
NMVOC 3 g/GJ
CO 25 g/GJ
0
2000
4000
6000
8000
10000
12000
14000
16000
18000
20000
1990
1992
1994
1996
1998
2000
2002
2004
������������
0
100
200
300
400
500
600
700
800
900
1000
� ���������������
Fuel rate
CO2 emission
���������� Time-series for off shore gas flaring and the CO2 emission in sector 1B2c ii Flaring, gas
The fuel consumption for offshore flaring was higher in 1999 due to the opening of new gas fields.
Besides 1999, consumption has been fairly stable for a number of years. The decrease from 15455 TJ in 1999 to 7311 TJ in 2005 represents a de-crease of around 50%.
��,��� $������������#�����������������������
Estimation of uncertainty is based on the Tier 1 methodology in IPCC Good Practice Guidance (IPCC, 2000). The results of the uncertainty es-timates are shown in Table 3.42.
Uncertainty of activity rates for oil and gas activities is 15%, referring to the GPG. The uncertainty of emission factors for CO2 is the uncertainty of emission factors for flaring. This emission factor uncertainty is 5% (GPG). Uncertainty with regard to CH4 and N2O emission factors is as-sumed to be 50% in both cases.
170
���������� Uncertainty of activity rates and emission factors
Please see Section 1.6 for the general description of QA/QC. The quality manual describes the concepts of quality work and definitions of suffi-cient quality, critical control points and a list of Points for Measuring (PMs).
2��������(����1���6����������� List of external data sources
The DEA is responsible for the official Danish energy statistics as well as reporting to the IEA. NERI regards the data as being complete and in ac-cordance with the official Danish energy statistics and IEA reporting. The uncertainties connected with estimating fuel consumption do not, therefore, influence the accordance between IEA data, the energy statis-tics and the dataset on SNAP level utilised by NERI. For the remainding datasets, it is assumed that the level of uncertainty is relatively small, ex-
Dataset Description AD or Emf. Reference Contact(s) Data agreement/ Comment
Data for offshore Gas and oil production. Dataset for production of oil, gas and number of plat-forms. CRF 1B2a
Activity data The Danish Energy Au-thority (DEA)
Katja Schar-mann
No formal data agreement.
Environmental report from DONG Gas and oil production. The amount of oil loaded onshore and emissions from raw oil tanks. CRF 1B2a
Activity data/emissions
DONG, 2006 Mike Robson No formal data agreement.
Luftemissioner fra raffinaderiet (Statoil)
Fuel consumption and emis-sion data. CRF 1B2a.
Activity data/emissions
Statoil Anik Olesen/Dan Juul Andersen
No formal data agreement.
Shell-raffinaderiet, Fredericia, SO2 og NOx emissoner samt fuelforbrug
Fuel consumption and emis-sion data. CRF 1B2a
Activity data/emissions
Shell Lis Rønnow Rasmussen
No formal data agreement.
Energiproducenttællingen.xls Energy consumption data for the refineries in Denmark. CRF 1B2a
Activity data DEA Peter Dal Formal data agree-ment.
Environmental indicators of the gas industry
Data for natural gas trans-mission/distribution and storage. CRF 1B2b.
Activity data and emissions
DGC Michael Oertenblad/ Jan K. Jensen
No formal data agreement.
Energy statistics The Danish energy statistics on SNAP level. CRF 1B2c.
Activity data DEA Peter Dal Data agreement in place
Emission factors Emission factors stems from a large number of sources
Emission fac-tors
See chapter regarding emission factors
Data Storage level 1
1. Accuracy DS.1.1.1 General level of uncertainty for every dataset including the reasoning for the specific values.
171
cept for the emissions from refineries. For further comments regarding uncertainties, see Chapter 3.5.3.
The uncertainty for external data is not quantified. The uncertainties of activity data and emission factors are quantified, see Chapter 3.5.3.
Systematic inter-country comparison has only been made on Data Stor-age Level 4. Refer to DS 4.3.2.
External data sources are the Danish Energy Authority and annual envi-ronmental reports from plants which are obligated to publish environ-mental reports. A summary of each dataset is not yet given.
All external data are stored in the inventory file system and are accessi-ble for all inventory staff members. Refer to Section 1.1.9.
Formal agreements are made with the DEA. Most of the other external data sources are available due to legal requirements in this regard. See Table. 3.44
See DS 1.3.1
Refer to Table 3.44 for general references. The references are available in the inventory file system. Refer to Section 1.1.9.
Data Storage
level 1
1. Accuracy DS.1.1.2 Quantification of the uncertainty level of every single data value including the reasoning for the specific values.
Data Storage level 1
2.Comparability DS.1.2.1 Comparability of the data values with similar data from other countries, which are compara-ble with Denmark, and evaluation of discrep-ancy.
Data Storage level 1
3.Completeness DS.1.3.1 Documentation showing that all possible na-tional data sources are included, by setting down the reasoning behind the selection of datasets.
Data Storage level 1
4.Consistency DS.1.4.1 The origin of external data has to be preserved whenever possible without explicit arguments (referring to other PMs)
Data Storage level 1
6.Robustness DS.1.6.1 Explicit agreements between the external insti-tution holding the data and NERI about the condition of delivery
Data Storage level 1
7.Transparency DS.1.7.1 Summary of each dataset including the reason-ing for selecting the specific dataset
Data Storage
level 1
7.Transparency DS.1.7.3 References for citation for any external data set have to be available for any single value in any dataset.
Data Storage level 1
7.Transparency DS.1.7.4 Listing of external contacts for every dataset.
172
Refer to Table 3.44
2����%�������(���1���6�
Refer to Section 1.7 in the Danish NIR and the QA/QC Section 3.5.3.
The uncertainty assessment of activity data and emission factors are dis-cussed in Section 1.7 concerning uncertainties.
The methodological approach is consistent with international guidelines and described in Section 3.5.2.
This PM has only been carried out for some of the sources, but will be completed for the key sources.
The calculations follow the principles in international guidelines.
Regarding the emissions from refineries, more detailed data material would be preferred.
No accessibility to critical data sources is lacking.
A change in calculating procedure would entail that an updated descrip-tion would be elaborated.
Data Processing level 1
1. Accuracy DP.1.1.1 Uncertainty assessment for every data source as input to Data Storage Level 2 in relation to type of variability (distribution as: normal, log normal or other type of variability)
Data Processing level 1
1. Accuracy DP.1.1.2 Uncertainty assessment for every data source as input to Data Storage level 2 in relation to scale of variability (size of variation intervals)
Data Processing level 1
1. Accuracy DP.1.1.3 Evaluation of the methodological approach using international guidelines
Data Processing level 1
1. Accuracy DP.1.1.4 Verification of calculation results using guideline values.
Data Processing level 1
2.Comparability DP.1.2.1 The inventory calculation has to follow the international guidelines suggested by UNFCCC and IPCC.
Data Processing level 1
3.CompletenessDP.1.3.1 Assessment of the most important quantitative knowledge which is lacking.
Data Processing level 1
3.CompletenessDP.1.3.2 Assessment of the most important cases where access is lacking with regard to critical data sources that could improve quantitative knowledge.
Data Processing level 1
4.Consistency DP.1.4.1 In order to keep consistency at a high level, an explicit description of the activities needs to accompany any change in the calculation procedure.
173
During data processing it is checked that calculations are performed cor-rectly. However, documentation for this needs to be elaborated.
A time-series, for activity data on SNAP level as well as emission factors is used to identify possible errors in the calculation procedure.
This PM has only been carried out for some of the sources.
There is a direct line between the external datasets, the calculation proc-ess and the input data used on Data Storage level 2. During the calcula-tion process, numerous controls are in place to ensure correctness, e.g. sum checks of the various stages in the calculation procedure.
Direct references to the NIR will be worked out.
References to external data sets will be worked out for all sources.
At present, a manual log table is not in place on this level. However, this feature will be implemented in the future. A manual log table is incorpo-rated in the national emissions database, Data Storage level 2.
To ensure a correct connection between data on level 2 to data on level 1, different controls are in place, e.g. control of sums and random tests.
Data Processing level 1
5.Correctness DP.1.5.1 Show at least once, by independent calcula-tion, the correctness of every data manipula-tion.
Data Processing level 1
5.Correctness DP.1.5.2 Verification of calculation results using time-series.
Data Processing level 1
5.Correctness DP.1.5.3 Verification of calculation results using other measures.
Data Processing level 1
5.Correctness DP.1.5.4 Shows one-to-one correctness between external data sources and the databases at Data Storage level 2.
Data Processing level 1
7.Transparency DP.1.7.1 The calculation principle and equations used must be described.
Data Processing level 1
7.Transparency DP.1.7.2 The theoretical reasoning for all methods must be described.
Data Processing level 1
7.Transparency DP.1.7.3 Explicit listing of assumptions behind all meth-ods.
Data Processing level 1
7.Transparency DP.1.7.4 Clear reference to data set at Data Storage level 1.
Data Processing level 1
7.Transparency DP.1.7.5 A manual log to collect information on recalcu-lations.
Data Storage level 2
5.Correctness DS.2.5.1 Documentation of a correct connection between all data types at level 2 to data at level 1
174
Data import is checked by use of sum control and random testing. The same procedure is applied every year in order to minimise the risk of data import errors.
!((����#�=7:='�� ��.���.!(���������������A list of QA/QC tasks to be performed directly in relation to the fugitive emission part of the Danish emission inventories will be prepared in 2007/2008, together with a time-table for the individual tasks.
��,�,� ���� �! ������
% ���()�(���1'�%��������*<��)�% ���(���2�Recalculation has been carried out according to the energy statistics pub-lished in 2006.
��,��� �!���������.���� ��#�������������
No improvements are planned in this sector.
��.�������.���'����������)���/��#���,�
Andersen, M.A. 1996: Elkraft, personal communication, letter 07-05-1996.
Christiansen, M. 1996: Elsam, personal communication, letter 07-05-1996.
Danish Energy Authority, 2006a: The Danish energy statistics aggregated to SNAP sectors. Not published.
Danish Energy Authority, 2006b: The Danish energy statistics, Available at http://www.ens.dk/graphics/UK_Facts_Figures/Statistics/yearly_s-tatistics/BasicData2005.xls (15-04-2007).
Danish Energy Authority, 2006c: The Danish energy statistics, Ener-giproducenttællingen 2005. Not published.
DONG, 2006: Annually environmental report from DONG.
EMEP/CORINAIR, 2004: Emission Inventory Guidebook 3rd edition, prepared by the UNECE/EMEP Task Force on Emissions Inventories and Projections, 2004 update. Available at http://reports.eea.eu.int/E-MEPCORINAIR4/en (15-04-07)
IPCC, 1997: Revised 1996 IPCC Guidelines for National Greenhouse Gas Inventories. Available at http://www.ipcc-nggip.iges.or.jp/public/gl-/invs6.htm (15-04-2007).
IPCC, 2000: Good Practice Guidance and Uncertainty Management in National Greenhouse Gas Inventories. Available at http://www.ipcc-nggip.iges.or.jp/public/gp/english/ (15-04-2007).
Data Storage level 2
5.Correctness DS.2.5.2 Check if a correct data import to level 2 has been made.
175
Jensen, B.G. & Lindroth, M. 2002: Kontrol af indberetning af CO2-udledning fra el-producenter i 2001, Carl Bro for Energistyrelsens 6. Kon-tor (in Danish).
Karll, B. 2003, Personal communication, e-mail 17-11-2003, Danish Gas Technology Centre.
Karll, B. 2005, Personal communication, e-mail 09-11-2005, Danish Gas Technology Centre.
Karll, B., 2006: Methane emission from the Danish gas distribution sys-tem. DGC note March 2006.
Kristensen, P.G. 2001: Personal communication, e-mail 10-04-2001, Dan-ish Gas Technology Centre.
Nielsen, M. & Illerup, J.B. 2003: Emissionsfaktorer og emissionsopgørelse for decentral kraftvarme. Eltra PSO projekt 3141. Kortlægning af emissi-oner fra decentrale kraftvarmeværker. Delrapport 6. Danmarks Miljøun-dersøgelser. 116 s. –Faglig rapport fra DMU nr. 442.(In Danish, with an English summary). Available at http://www.dmu.dk/1_viden/2_Publi-kationer/3_fagrapporter/rapporter/FR442.pdf (15-04-2007).
Oertenblad, M. 2006, personal communication, e-mail 2006, Danish Gas Technology Centre
Pulles, T. & Aardenne, J.v. 2001: Good Practice Guidance for LRTAP Emission Inventories, 7. November 2001. Available at http://reports.ee-a.eu.int/EMEPCORINAIR4/en/BGPG.pdf (15-04-2007).
176
/� �#!����� �����������1'�%� �������2�
/�*� 6������&��.�����������
The aim of this chapter is to present industrial emissions of greenhouse gases, not related to generation of energy. An overview of the sources identified is presented in Table 4.1 with an indication of the contribution to the industrial part of the emission of greenhouse gases in 2005. The emissions are extracted from the CRF tables.
The subsectors ������#�*������ , including the estimates (2A), constitutes 66%, ���+���#����� �� (2B) constitutes below 1%, ����#�*��������� consti-tutes below 1%, and ��� �+*����� ��� ��#����"�� � ����9�� (2F) constitutes 34% of the industrial emission of greenhouse gases. The total emission of greenhouse gases (excl. LUCF) in Denmark is estimated to 63.95 Mt CO2-eq., of which industrial processes contribute with 2.50 Mt CO2-eq. (3.9%). The emission of greenhouse gases from industrial processes from 1990-2005 are presented in Figure 4.1.
��������� Overview of industrial greenhouse gas sources (2005).
Process IPCC Code Substance Emission kton CO2-eq. %
Cement 2A CO2 1456 58.2% Refrigeration 2F HFCs+PFCs 664 26.6% Foam blowing 2F HFCs 146 5.83% Lime and bricks 2A CO2 110 4.39% Limestone and dolomite use 2A CO2 60.7 2.43% Metal production 2C CO2 15.6 0.62% Other (container glass, glass wool) 2A CO2 12.6 0.50% Electrical equipment 2F SF6 12.5 0.50% Other (laboratories, double glaze windows) 2F SF6 9.25 0.37% Aerosols / Metered dose inhalers 2F HFCs 8.77 0.35% Catalysts / fertilisers 2B CO2 3.01 0.12% Road paving 2A CO2 1.84 0.073% Asphalt roofing 2A CO2 0.024 0.0010% Nitric acid 2B N2O 0 0.00% Total 2500 100.00%
177
The key sources in the industrial sector constitute 1.3-2.3% of the total emission of greenhouse gases. The trends in greenhouse gases from the industrial sector/subsectors are presented in Table 4.2 and they will be discussed subsector by subsector below. The emissions are extracted from the CRF tables.
0
1
2
3
4
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
K���
A���'6
�
��>�
���������� Emission of greenhouse gases from industrial processes (CRF Sector 2) from 1990-2005.
178
A number of improvements have been planned and are in progress, e.g. inclusion of iron foundries.
/��� A���� ����#!����1�72�
/���*� �!��������(����#����������
The subsector ������#�*������ (2A) cover the following processes:
• Production of cement (SNAP 040612) • Production of lime (quicklime) (SNAP 040614)
��������� Emission of greenhouse gases from industrial processes in different subsectors from 1990-2005.
� �� � � � �� � � � �� � � � � � � �� �
��� (kt CO2)
A. Mineral Products 1072 1246 1366 1383 1406 1407 1517 1685 1682 1610
B. Chemical Industry 0.80 0.80 0.80 0.80 0.80 0.80 1.45 0.87 0.56 0.58
C. Metal Production 28.4 28.4 28.4 31.0 33.5 38.6 35.2 35.0 42.2 43.0
B. Chemical Industry 3.36 3.08 2.72 2.56 2.60 2.92 2.69 2.74 2.60 3.07
���� (kt CO2 eq.)
F. Consumption of Halo-carbons and SF6 - - 3.44 93.9 135 218 329 324 412 504
���� (kt CO2 eq.)
F. Consumption of Halo-carbons and SF6 - - - - 0.053 0.50 1.66 4.12 9.10 12.5
��� (kt CO2 eq.)
F. Consumption of Halo-carbons and SF6 44.5 63.5 89.2 101 122 107 61.0 73.1 59.4 65.4
� ����� ���� ����� ���� ����� ��� � �
����������
��� (kt CO2)
A. Mineral Products 1640 1660 1696 1571 1728 1641
B. Chemical Industry 0.65 0.83 0.55 1.05 3.01 3.01
C. Metal Production 40.7 46.7 NA,NO NA,NO NA,NO 15.6
Total 1682 1708 1697 1572 1731 1659
����
- - - - - -
��� (kt N2O)
B. Chemical Industry 3.24 2.86 2.50 2.89 1.71 0.00
���� (kt CO2 eq.)
F. Consumption of Halo-carbons and SF6 606 650 676 700 754 606
���� (kt CO2 eq.)
F. Consumption of Halo-carbons and SF6 17.9 22.1 22.2 19.3 15.9 13.9
��� (kt CO2 eq.)
F. Consumption of Halo-carbons and SF6 59.2 30.4 25.0 31.4 33.1 21.8
179
• Production of bricks and tiles (SNAP 040614) • Limestone and dolomite use (SNAP 040618) • Roof covering with asphalt materials (SNAP 040610) • Road paving with asphalt (SNAP 040611) • Production of container glass/glass wool (SNAP 040613 Production of cement is identified as a key source; see ����.� �:� ;�� ����� .
The time-series for the emission of CO2 from ������#� *������ (2A) are presented in Table 4.3. The emissions are extracted from the CRF tables and the values are rounded.
The increase in CO2 emission is most significant for the production of cement. From 1990 to 2005, the CO2 emission increased from 882 to 1456 kt CO2, i.e. by 65%. The maximum emission occurred in 2004 and consti-tuted 1539 kt CO2; see Figure 4.2.
��������� Time-series for emission of CO2 (kt) from Mineral products (2A).
� � � � � � � � � � � � � � � � �
1. Production of Cement 882 1088 1192 1206 1192 1204 1.282 1441 1452 1365
2. Production of Lime and Bricks 152 118 132 129 144 132 130 139 122 126
3. Limestone and dolomite use 18.1 23.2 25.2 32.6 53.1 55.2 89,3 89.6 91.2 99.2
The increase can be explained by the increase in the annual production. The emission factor has only changed slightly as the distribution be-tween types of cement especially grey/white cement has been almost constant from 1990-1997.
/����� A���#� �(��� ����!���
The CO2 emission from the production of cement has been estimated from the annual production of cement expressed as TCE (total cement equivalents6) and an emission factor estimated by the company (Aalborg Portland, 2006). The emission factor has been estimated from the loss of ignition determined for the different kinds of clinkers produced, com-bined with the volumes of grey and white cements produced. Determi-nation of loss of ignition takes into account all the potential raw materi-als leading to release of CO2 and omits the Ca-sources leading to genera-tion of CaO in cement clinker without CO2 release. The applied method-ology is in accordance with EU guidelines in calculation of CO2 emis-sions (Aalborg Portland, 2006). However, detailed information on CO2 release is expected to be available for the next inventory due to inclusion of data supplied by the company to the EU ETS.
The CO2 emission from the production of burnt lime (quicklime) as well as hydrated lime (slaked lime) has been estimated from the annual pro-duction figures, registered by Statistics Denmark, and emission factors. The emission factors applied are 0.785 kg CO2/kg CaO as recommended by IPCC (IPCC, 1997, vol. 3, p. 2.8) and 0.541 kg CO2/kg hydrated lime (calculated from company information on composition of hydrated lime (Faxe Kalk, 2003)).
The CO2 emission from the production of bricks and tiles has been esti-mated from information on annual production registered by Statistics Denmark, corrected for amount of yellow bricks and tiles. This amount is unknown and, therefore, is assumed to be 50%. The content of CaCO3 and a number of other factors determine the colour of bricks and tiles and, in the present estimate, the average content of CaCO3 in clay has
6 TCE (total cement equivalent) expresses the total amount of cement pro-duced for sale and the theoretical amount of cement from the amount of clinkers produced for sale.
0,00
1,00
2,00
3,00
4,00
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
����
��������
���������� Emission of CO2 from cement production.
181
been assumed to be 18%. The emission factor (0.44 kg CO2/kg CaCO3) is based on stoichiometric determination.
The CO2 emission from the production of container glass/glass wool has been estimated from production statistics published in environmental reports from the producers (Rexam Holmegaard, 2006; Saint-Gobain Is-over, 2006) and emission factors based on release of CO2 from specific raw materials (stoichiometric determination).
The CO2 emission from consumption of limestone for fluegas cleaning has been estimated from statistics on generation of gypsum (wet flue gas cleaning processes) and the stoichiometric relations between gypsum and release of CO2:
SO2 (g) + ½O2 (g) + CaCO3 (s) + 2H2O → CaSO4,2H2O (s) + CO2 (g)
and the emission factor is: 0.2325 ton CO2/tonne gypsum.
Statistics on the generation of gypsum from power plants are compiled by Energinet.dk (2006). Information on the generation of gypsum at waste incineration plants does not explicitly appear in the Danish waste statistics (Miljøstyrelsen, 2006). However, the total amount of waste products generated can be found in the statistics. The amount of gypsum is calculated by using information on flue gas cleaning systems at Danish waste incineration plants (Illerup et al., 1999; Nielsen & Illerup, 2002) and waste generation from the different flue gas cleaning systems (Hjel-mar & Hansen, 2002).
The CO2 emission from the production of expanded clay products has been estimated from production statistics compiled by Statistics Den-mark and an emission factor of 0.045 tonne CO2/tonne product.
The CO2 emission from the refining of sugar is estimated from produc-tion statistics for sugar and a number of assumptions: consumption of 0.02 tonne CaCO3/tonne sugar and precipitation of 90% CaO resulting in an emission factor at 0.0088 tonne CO2/tonne sugar.
The indirect emission of CO2 from asphalt roofing and road paving has been estimated from production statistics compiled by Statistics Den-mark and default emission factors presented by IPCC (1997) and EMEP/CORINAIR (2004). The default emission factors, together with the calculated emission factor for CO2, are presented in Table 4.4.
��������� Default emission factors for application of asphalt products.
Road paving with asphalt
Use of cutback asphalt Asphalt roofing
CH4 g/tonnes 5 0 0
CO g/tonnes 75 0 10
NMVOC g/tonnes 15 64935 80
Carbon content fraction
of NMVOC % 0.667 0.667 0.8
Indirect CO2 kg/tonnes 0.168 159 0.250
182
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The time-series are presented in Table 4.3. The methodology applied for the years 1990-2005 is considered to be consistent as the emission factor has been determined by the same approach for all years. The emission factor has only changed slightly as the distribution between types of ce-ment, especially grey/white cement, has been almost constant from 1990-2005. Furthermore, the activity data originates from the same com-pany for all years.
For the production of lime and bricks, as well as container glass and glass wool, the same methodology has also been applied for all years. The emission factors are based either on stoichiometric relations or on a standard assumption of CaCO2-content of clay used for bricks. The source for the activity data is, for all years, Statistics Denmark.
The source-specific uncertainties for mineral products are presented in Section 4.7. The overall uncertainty estimate is presented in Section 1.7.
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The estimation of CO2 release from the production of bricks based on an assumption of 50% yellow bricks has been verified by comparing the es-timate with actual information on emission of CO2 from calcination of lime compiled by the Danish Energy Authority (DEA) (Danish Energy Authority, 2004). The information from the companies (tile-/brickworks; based on measurements of CaCO3 content of raw material) has been compiled by DEA in order to allocate a CO2 quota to Danish companies with the purpose of future reductions. The result of the comparison is presented in Figure 4.3.
Figure 4.3 shows a reasonable correlation between the estimated and measured CO2 emission.
/���,� ���� �! ������
No source-specific recalculations have been performed regarding emis-sions from mineral products.
0
5000
10000
15000
20000
25000
30000
35000
1998 1999 2000 2001 2002
����
�������
Estimate
Measured
���������� Estimated and “measured” CO2 emission from tile-/brickworks; “measured” means information provided to the Danish Energy Authority by the individual companies (Danish Energy Authority, 2004).
183
/����� �!���������.���� ��#�������������
Regarding the production of cement, dialogue with the company will continue with the aim to obtain more detailed information on production statistics (i.e. production of different types of clinker) and corresponding emission factors. In addition to the dialogue with the company, informa-tion supplied by the company to EU ETS will be included.
Production statistics for glass and glass wool as well as information on consumption of raw materials will be completed for 1990-1995.
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/���*� �!��������(����#����������
The subsector ���+���#����� �� (2B) covers the following processes:
• Production of nitric acid/fertiliser (SNAP 040402/040407) • Production of catalysts/fertilisers (SNAP 040416/040407) Production of nitric acid is identified as a key source.
The time-series for emission of CO2 and N2O from ���+���#����� �� (2B) are presented in Table 4.5.
�������� Time-series for emission of greenhouse gasses from Chemical industry (2B).
��� � � � � � � � � � � � � � � � � �
2. Nitric acid production (kt N2O)
3.36 3.08 2.72 2.56 2.60 2.92 2.69 2.74 2.60 3.07
2. Nitric acid production (kt CO2 eq.) 1043 955 844 795 807 904 834 848 807 950
Total (kt CO2 eq.) 1044 956 844 796 807 905 836 849 807 951
���� ���� ���� ���� ���� ���
����������
2. Nitric acid production (kt N2O)
3.24 2.86 2.50 2.89 1.71 0
2. Nitric acid production (kt CO2 eq.) 1004 885 774 895 531 0
5. Other (kt CO2) 0.65 0.83 0.55 1.05 3.01 3.01
Total (kt CO2 eq.) 1004 886 775 896 534 3.01
The emissions are extracted from the CRF tables and the values are rounded.
The emission of N2O from nitric acid production is the most considerable source of GHG from the chemical industry. The trend for N2O from 1990 to 2003 shows a decrease from 3.36 to 2.89 kt, i.e. -14%, and a 40% de-crease from 2003 to 2004. However, the activity and the corresponding emission show considerable fluctuations in the period considered and the decrease from 2003 to 2004 can be explained by the closing of the plant in the middle of 2004.
184
From 1990 to 2005, the emission of CO2 from the production of cata-lysts/fertilisers has increased from 0.80 to 3.01 kt, due to an increase in the activity as well as changes in raw material consumption.
/����� A���#� �(��� ����!���
The N2O emission from the production of nitric acid/fertiliser is based on measurement for 2002. For the previous years, the N2O emission has been estimated from annual production statistics from the company and an emission factor of 7.5 kg N2O/tonne nitric acid, based on the 2002 emission measured (Kemira Growhow, 2004). The production of nitric acid ceased in the middle of 2004.
The CO2 emission from the production of catalysts/fertilisers is based on information in an environmental report from the company (Haldor Top-søe, 2006), combined with personal contacts. In the environmental re-port, the company has estimated the amount of CO2 from the process and the amount from energy conversion. Based on information from the company, the emission of CO2 has been calculated from the composition of raw materials used in the production (for the years 1990 and 1996-2004) and for 2005 assumed to be the same as in 2004 based on the same activity (produced amount). For the years 1991-1995, the production, as well as the CO2 emission, has been assumed to remain the same as in 1990.
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The time-series are presented in Table 4.5. The applied methodology re-garding N2O is considered to be consistent. The activity data is based on information from the specific company. The emission factor applied has been constant from 1990 to 2001 and is based on measurements in 2002. The production equipment has not been changed during the period.
The estimated CO2 emissions are considered to be consistent as they are based on stoichiometric relations combined with company assumptions for the years 1991-1995.
The source-specific uncertainties for the chemical industry are presented in Section 4.7. The overall uncertainty estimate is presented in Section 1.7.
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No source-specific recalculations have been performed regarding emis-sions from the chemical industry.
/���,� �!���������.���� ��#�������������
No improvements are planned for this sector.
185
/�/� A��� ����#!�����1�'2�
/�/�*� �!��������(����#����������
The subsector ����#�*��������� (2C) covers the following process:
• Steelwork (SNAP 040207) The time-series for emission of CO2 from ����#� *��������� (2C) is pre-sented in Table 4.6. The emissions are extracted from the CRF tables and the values presented are rounded.
From 1990 to 2001, the CO2 emission from the electro-steelwork has in-creased from 28 to 47 kt, i.e. by 68%. The increase in CO2 emission is similar to the increase in the activity as the consumption of metallurgical coke per amount of steel sheets and bars produced has almost been con-stant during the period. The electro-steelwork reopened and closed down again in 2005.
/�/��� A���#� �(��� ����!���
The CO2 emission from the consumption of metallurgical coke at steel-works has been estimated from the annual production of steel sheets and steel bars combined with the consumption of metallurgical coke per pro-duced amount (Stålvalseværket, 2002). The carbon source is assumed to be coke and all the carbon is assumed to be converted to CO2 as the car-bon content in the products is assumed to be the same as in the iron scrap. The emission factor (3.6 tonnes CO2/tonne metallurgical coke) is based on values in the IPCC-guidelines (IPCC (1997), vol. 3, p. 2.26). Emissions of CO2 for 1990-1991 and for 1993 have been determined with extrapolation and interpolation, respectively.
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The time-series (see Table 4.6) is considered to be consistent as the same methodology has been applied for the whole period. The activity, i.e. amount of steel sheets and bars produced as well as consumption of metallurgical coke, has been published in environmental reports. The emission factor (consumption of metallurgical coke per tonnes of prod-uct) has been almost constant from 1994 to 2001. For the remaining years, the same emission factor has been applied. In 2002, production stopped. For 2005 the production has been assumed to be one third the produc-tion in 2001 as the steelwork was operating between 4 and 6 months in 2005.
�������� Time-series for emission of CO2 (kt) from Metal production (2C).
��� � � � � � � � � � � � � � � � � �
1. Iron and steel production 28.4 28.4 28.4 31.0 33.5 38.6 35.2 35.0 42.2 43.0
���� ���� ���� ���� ���� ���
����������
1. Iron and steel production 40.7 46.7 NA,NO NA,NO NA,NO 15.6
186
The source-specific uncertainties for the metal production are presented in Section 4.7. The overall uncertainty estimate is presented in Section 1.7.
/�/�/� ���� �! ������
No source-specific recalculations have been performed regarding emis-sions from the metal production.
/�/�,� �!���������.���� ��#�������������
Production statistics and information on consumption of raw materials will be completed for 1990-1993. The mass balance (i.e. amounts of steel bars and steel sheets produced as well as consumption of metallurgical coke) for the steelworks will be improved/verified.
The emission of CO2 from iron foundries is not included at the moment. However, this source will be investigated and included.
/�,� ;��#!������.�B� ����"����#� %��1��2�
There is no production of Halocarbons or SF6 in Denmark.
The sub-sector ��� �+*����� ��� ��#����"�� � ����9�� (2F) includes the fol-lowing source categories and the following F-gases of relevance for Dan-ish emissions:
• 2C: SF6 used in Magnesium Foundries SNAP 040304: SF6; see Table 4.7
• 2F: Foam blowing SNAP 060504: HFC134a, 152a; see Table 4.9 • 2F: Aerosols/Metered dose inhalers SNAP 060506: HFC134a; see
Table 4.10 • 2F: Production of electrical equipment SNAP 060507: SF6; see Table
4.11 • 2F: Other processes SNAP 060508: SF6, PFC (C3F8); see Table 4.12 A quantitative overview is given below for each of these source catego-ries and each F-gas, showing their emissions in tonnes through the times-series. The data is extracted from the CRF tables that form part of this submission and the data presented is rounded values. It must be no-ticed that the inventories for the years 1990-1993 (1994) might not cover emissions of these gases in full. The choice of base-year for these gases is 1995 for Denmark.
187
��������� SF6 used in magnesium foundries (t).
��� � � � � � � � � � � � � � � � � �
SF6 used in magne-sium foundries 1.30 1.30 1.30 1.50 1.90 1.50 0.40 0.60 0.70 0.70
���� ���� ���� ���� ���� ���
����������
SF6 used in magne-sium foundries 0.89 NO NO NO NO NO
��������� Consumption of HFCs and PFC in refrigeration and air condition systems (t).
The emission of SF6 has been decreasing in recent years due to the fact that activities under Magnesium Foundry no longer exist and due to a decrease in the use of electric equipment. Also, a decrease in "other" oc-curs, which for SF6 is used in window plate production use, laboratories and in the production of running shoes.
The emission of HFCs increased rapidly in the 1990s and, thereafter, in-creased more modestly due to a modest increase in the use of HFCs as a refrigerant and a decrease in foam blowing. The F-gases have been regu-lated in two ways since 1 March 2001. For some types of use there is a ban on use of the gases in new installations and for other types of use, taxation is in place. These regulations seem to have influenced emissions so that they now only increase modestly.
Table 4.13 quantifies an overview of the emissions of the gases in CO2-eq. The reference is the trend table as included in the CRF table for year 2005.
���������� Time-series for emission of HFCs, PFCs and SF6 (kt CO2-eq.).
The decrease in the SF6 emission has brought its emissions in CO2-eq. down to the level of PFC. Overall, and for all uses, the most dominant group by far is HFCs. In this grouping, HFCs constitute a key source, both with regard to the key source level and trend analysis. In the level
189
analysis, the HFC group is number 16 out of 21 key sources and contrib-uted, in 2005, 1.3% to the national total.
/����� A���#� �(��� ����!���
The data for emissions of HFCs, PFCs, and SF6 has been obtained in con-tinuation on work on inventories for previous years. The determination includes the quantification and determination of any import and export of HFCs, PFCs, and SF6 contained in products and substances in stock form. This is in accordance with the IPCC guidelines (IPCC (1997), vol. 3, p. 2.43ff), as well as the relevant decision trees from the IPCC Good Prac-tice Guidance (IPCC, 2000) p. 3.53ff).
For the Danish inventories of F-gases, a Tier 2 bottom-up approach is ba-sically used. As for verification using import/export data, a Tier 2 top-down approach is applied. In an annex to the F-gas inventory report 2005 (Danish Environmental Protection Agency, 2007), there is a specifi-cation of the approach applied for each sub-source category.
The following sources of information have been used:
• Importers, agency enterprises, wholesalers and suppliers • Consuming enterprises, and trade and industry associations • Recycling enterprises and chemical waste recycling plants • Statistics Denmark • Danish Refrigeration Installers’ Environmental Scheme (KMO) • Previous evaluations of HFCs, PFCs and SF6 Suppliers and/or producers provide consumption data of F-gases. Emis-sion factors are primarily defaults from the GPG, which are assessed to be applicable in a national context. In case of commercial refrigerants and Mobile Air Condition (MAC), national emission factors are defined and used.
Import/export data for sub-source categories where import/export is relevant (MAC, fridge/freezers for household) are quantified on esti-mates from import/export statistics of products + default values of the amount of gas in the product. The estimates are transparent and de-scribed in the annex to the report referred to above.
The Tier 2 bottom-up analysis used for determination of emissions from HFCs, PFCs, and SF6 covers the following activities:
• Screening of the market for products in which F-gases are used • Determination of averages for the content of F-gases per product unit • Determination of emissions during the lifetime of products and dis-
posal • Identification of technological development trends that have signifi-
cance for the emission of F-gases • Calculation of import and export on the basis of defined key figures,
and information from Statistics Denmark on foreign trade and indus-try information.
The determination of emissions of F-gases is based on a calculation of the actual emission. The actual emission is the emission in the evaluation
190
year, accounting for the time lapse between consumption and emission. The actual emission includes Danish emissions from production, from products during their lifetimes and from waste products.
Consumption and emissions of F-gases are, whenever possible, deter-mined for individual substances, even though the consumption of cer-tain HFCs has been very limited. This has been carried out to ensure transparency of evaluation in the determination of GWP values. How-ever, the continued use of a category for ��������� has been necessary since not all importers and suppliers have specified records of sales for individual substances.
The potential emissions have been calculated as follows:
Potential emission = import + production - export - destruction/treat-ment.
The substances have been accounted for in the survey according to their trade names, which are mixtures of HFCs used in the CRF, etc. In the transfer to the "pure" substances used in the CRF reporting schemes, the following ratios have been used; see Table 4.14.
The national inventories for F-gases are provided and documented in a yearly report (Environmental Protection Agency, 2007). Furthermore, de-tailed data and calculations are available and archived in an electronic version. The report contains summaries of methods used and informa-tion on sources as well as further details on methodologies.
Activity data is described in a spreadsheet for the current year.
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The time-series for emission of Halocarbons and SF6 are presented in Section 4.6.1. The time-series are consistent as regards methodology. No potential emission estimates are included as emissions in the time-series and the same emission factors are used for all years.
No appropriate measures of uncertainties have been established and no uncertainty estimates following the GPG procedures have been devel-oped for the F-gas calculations, to date.
In general, uncertainty in inventories will arise through at least three dif-ferent processes:
���������� Content (w/w%) of “pure” HFC in HFC-mixtures, used as trade names.
1. Uncertainties from definitions (e.g. incomplete, unclear, or faulty definition of an emission or uptake);
2. Uncertainties from natural variability of the process that produces an emission or uptake;
3. Uncertainties resulting from the assessment of the process or quan-tity depending on the method used: (i) uncertainties from measuring; (ii) uncertainties from sampling; (iii) uncertainties from reference data that may be incompletely described, and (iv) uncertainties from expert judgement.
Uncertainties due to poor definitions are not expected to be an issue in the F-gas inventory. The definitions of chemicals, the factors, sub-source categories in industries etc. are well defined.
Uncertainties from natural variability are likely to occur over the short-term while estimating emissions in individual years. But over a longer time period, 10-15 years, these variabilities level out in the total emission. This is due to that input data (consumption of F-gases) is known and is valid data, and has no natural variability due to the chemicals stabile na-ture.
Uncertainties that arise due to imperfect measurement and assessment are probably an issue for the:
• emission from MAC (HFC-134a) • emission from commercial refrigerants (HFC-134a). Due to the limited knowledge for these sources, the expert assessment of consumption of F-gases can lead to inexact values of the specific con-sumption of F-gases.
The uncertainty varies from substance to substance. Uncertainty is great-est for HFC-134a due to its widespread application in products that are imported and exported. The greatest uncertainty in application is ex-pected to arise from consumption of HFC-404a and HFC-134a in com-mercial refrigerators and mobile refrigerators. The uncertainty involved in year-to-year data is influenced by the uncertainty associated with the rates at which the substances are released. This results in significant dif-ferences in the emission determinations in the short-term (approx. five years); differences that balance in the long-term.
The source-specific uncertainties for consumption of halocarbons and SF6 are presented in Section 4.7. The overall uncertainty estimate is pre-sented in Section 1.7.
/���/� =7:='��#�����.�������
'����������.��������������������!��(�#�..���������������Inventory agencies should use the Tier 1 potential emissions method for a check on the Tier 2 actual emission estimates. Inventory agencies may consider developing accounting models that can reconcile potential and actual emission estimates and which may improve the determination of emission factors over time.
192
This comparison was carried out in 1995-1997 and, for all three years, it shows a difference of approx. factor 3 higher emission by using potential emission estimates.
Inventory agencies should compare bottom-up estimates with the top-down Tier 2 approach, since bottom-up emission factors have the highest associated uncertainty. This technique will also minimise the possibility that certain end-uses are not accounted for in the bottom-up approach.
This comparison has not been developed.
������ ����������#���������For the Tier 2a (bottom-up) method, inventory agencies should evaluate the QA/QC procedures associated with estimating equipment and product inventories to ensure that they meet the general procedures out-lined in the QA/QC plan and that representative sampling procedures are used. This is particularly important for the ODS (Ozone Depleting Substances)-substitute subsectors because of the large populations of equipment and products.
The spreadsheets containing activity data have incorporated several data-control mechanisms, which ensure that data estimates do not con-tain calculation failures. A very comprehensive QC procedure on the data in the model for the whole time-series has been carried for the pre-sent submission in connection with the process which provided, (1) data for the CRF background tables 2(II).F. for the years (1993)-2002 and (2) data for potential emissions in CRF tables 2(I). This procedure consisted of a check of the input data for the model for each substance. As regards the HFCs, this checking was carried out in relation to their trade names. Conversion was made to the HFC substances used in the CRF tables, etc. A QC was that emission of the substances could be calculated and checked comparing results from the substances as trade names and as the "no-mixture" substances used in the CRF.
��������.������������Emission factors used for the Tier 2a (bottom-up) method should be based on country-specific studies. Inventory agencies should compare these factors with the default values. They should determine if the coun-try-specific values are reasonable, given similarities or differences be-tween the national source category and the source represented by the de-faults. Any differences between country-specific factors and default fac-tors should be explained and documented.
Country-specific emission factors are explained and documented for MAC and commercial refrigerants and SF6 in electric equipment. Sepa-rate studies have been carried out and reported. For other sub-source categories, the country-specific emission factors are assessed to be the same as the IPCC default emission factors.
�������������As the F-gas inventory is developed and made available in full in spreadsheets, where HFCs data relate to trade names, special procedures are performed to check the full possible correctness of the transformation to the CRF-format through Access databases.
193
���� �! ������No source-specific recalculations have been performed regarding emis-sions of F-gases.
/���,� ; ��#�������������
It is planned to improve uncertainty estimates as well as the information on the choice of EFs and the specific approaches applied.
/��� $���������
The source-specific uncertainties for industrial processes are presented in Table 4.15. The uncertainties are based on IPCC guidelines combined with assessment of the individual processes.
The producer has delivered the activity data for production of cement as well as calculated the emission factor based on quality measurements. The uncertainties on activity data and emission factors are assumed to be 1% and 2%, respectively.
The activity data for production of lime and bricks are based on informa-tion compiled by Statistics Denmark. Due to the many producers and the variety of products, the uncertainty is assumed to be 5%. The emission factor is partly based on stoichiometric relations and partly on an as-sumption of the number of yellow bricks. The last assumption has been verified (see Section Table 4.15). The combined uncertainty is assumed to be 5%.
The producers of glass and glass wool have registered the consumption of - raw materials containing carbonate. The uncertainty is assumed to be 5%. The emission factors are based on stoichiometric relations and, there-fore, uncertainty is assumed to be 2%.
The producers have registered the production of nitric acid during many years and, therefore, the uncertainty is assumed to be 2%. The measure-ment of N2O is problematic and is only carried out for one year. There-fore, uncertainty is assumed to be 25%.
The uncertainty for the activity data as well as for the emission factor is assumed to be 5% for production of catalysts/fertilisers and iron and steel production.
The emission of F-gases is dominated by emissions from refrigeration equipment and, therefore, the uncertainties assumed for this sector will be used for all the F-gases. The IPCC propose an uncertainty at 30-40% for regional estimates. However, Danish statistics have been developed over many years and, therefore, the uncertainty on activity data is as-sumed to be 10%. The uncertainty on the emission factor is, on the other hand, assumed to be 50%. The base year for F-gases for Denmark is 1995.
194
/�4� =!� �������!����:>!� ��������� �1=7:='2�
The approach used for quality assurance/quality control (QA/QC) is presented in Chapter 1.6. The present chapter presents QA/QC consid-erations for industrial processes based on a series of Points of Measuring (PMs); see Section 1.6.
The uncertainty assessment has been performed on Tier 1 level by using default uncertainty factors. The applied uncertainty factors are presented in Table 4.16.
See DS.1.1.1. As Tier 1 and default uncertainty factors are applied, the individual datasets have not been assessed.
Comparability of the data has not been performed at “Data Storage level 1”. However, investigation of comparability at CRF level is in progress.
The applied data sets are presented in Table 4.16.
��������� Uncertainties on activity data and emission factors as well as overall trend uncertainties for the dif-ferent greenhouse gases.
Activity data uncertainty
Emission factor uncertainty
%
CO2 %
N2O %
HFCs3 %
PFCs3 %
SF63
%
2A1. Production of Cement 1 2
2A2. Production of Lime and Bricks 5 5
2A3. Limestone and dolomite use 5 5
2A5. Asphalt roofing 5 25
2A6. Road paving with asphalt 5 25
2A7. Other1 5 2
2B2. Nitric acid production 2 25
2B5. Other2 5 5
2C1. Iron and Steel production 5 5
2F. Consumption of HFC 10 50
2F. Consumption of PFC 10 50
2F. Consumption of SF6 10 50
Overall uncertainty in 2005 2.018 25.084 50.99 50.99 50.99
Trend uncertainty 2.091 1.4394 52.30 391.5 2.866
1. Production of container glass and glass wool.
2. Production of catalysts/fertilisers.
3. The base year for F-gases is for Denmark 1995.
4. 2004. The production closed down in the middle of 2004.
Data Storage level 1
1. Accuracy DS.1.1.1 General level of uncertainty for every dataset including the reasoning for the specific val-ues.
Data Storage level 1
1. Accuracy DS.1.1.2 Quantification of the uncertainty level of every single data value including the reason-ing for the specific values.
Data Storage level 1
2.Comparability DS.1.2.1 Comparability of the data values with similar data from other countries, which are compa-rable with Denmark, and evaluation of dis-crepancy.
195
The data sources - in general - can be grouped as follows:
• Company specific environmental reports • Personal communication with individual companies • Company specific information compiled by Danish Energy Authority
in relation to the EU-ETS • Industrial organisations • Statistics Denmark
Data Storage level 1
3.Completeness DS.1.3.1 Documentation showing that all possible national data sources are included setting down the reasoning behind the selection of datasets.
��������� �Applied data sets.
File or folder name Description AD or E Reference Contact(s) Comment
Danisco Assens gr2005-2006.pdf AD www.danisco.dk
www.cvr.dk
AD used for estimation of production at three different locations 1990-1995.
Danisco Nakskov gr2005-2006.pdf AD www.danisco.dk
www.cvr.dk
AD used for estimation of production at three different locations 1990-1995.
Danisco Nykøbing gr2005-2006.pdf AD www.danisco.dk
www.cvr.dk
AD used for estimation of production at three different locations 1990-1995.
Faxe_Kalk-brandt_kalk.pdf Chemical compo-sition of product.
www.faxekalk.dk
Faxe_Kalk-hydratkalk_191103.pdf Chemical compo-sition of product.
www.faxekalk.dk
Haldor Topsoe gr2005.pdf AD, E www.cvr.dk
Haldor Topsoe 1990.xls E Haldor Topsøe Allan Willumsen
Haldor Topsoe – emissioner 1996 – 2004.xls
E Haldor Topsøe Allan Willumsen
Kemira GR2003.pdf AD, E www.kemira-growhow.com
Rexam Glas Holmegaard gr2005.pdf E www.cvr.dk
Rockwool mr2005 I.pdf
Rockwool mr2005 II.pdf
AD www.cvr.dk
Saint Gobain – Miljøredegørelse 2005.pdf
AD,E Saint-Gobain Isover
www.isover.dk
Anette Åkesson
Stålvalseværket (2002) – paper version.
AD, E Stålvalseværket
Aalborg Portland miljore-degorelse_2005.pdf
AD, E www.aalborg-portland.dk
Aalborg Portland energy 2000-2004 answer.xls
AD Aalborg Portland Henrik Møller Thomsen
DS produktion af aluminium I.xls AD Danmarks Statistik; www.statistikbanken.dk
DS produktion af klinker + letbe-ton.xls
AD Danmarks Statistik; www.statistikbanken.dk
DS produktion af sukker.xls AD Danmarks Statistik; www.statistikbanken.dk
DS produktion af øl.xls AD Danmarks Statistik; www.statistikbanken.dk
196
• Secondary literature • IPCC guidelines
The environmental reports contribute with company-specific emission factors, technical information and, in some cases, activity data. The envi-ronmental reports are primarily used for large companies and, for some companies, are supplemented with information from personal contacts, especially for completion of the time-series for the years before the legal requirement to prepare environmental reports (i.e. prior to 1996).
Statistics Denmark is used as source for activity data as they are able to provide consistent data for the period 1990-2005. In the cases where the statistics do not contain transparent data, statistics from industrial or-ganisations are used to generate to required activity data.
For many of the processes, the default emission factors are based on chemical equations and are, therefore, the best choice. In some cases, the default EF has been modified in order to reflect local conditions.
Secondary literature may be used in the interpretation or in disaggrega-tion of the public statistics.
See DS.1.4.1. Consistency is secured by application of the same data source over the period in question, e.g. activity data from Statistics Den-mark, or by using personal contacts in the individual companies to ob-tain activity data for the period when environmental reports were not mandatory. For some activities, statistics compiled by industrial organi-sations were applied.
An agreement regarding inclusion of information - compiled by Danish Energy Authority for EU-ETS - in the Danish GHG-inventory has been signed. The data reported to DEA for the year 2006 is expected to be available for the next GHG-inventory.
The datasets applied are presented in Table 4.16. For the reasoning be-hind their selection, see DS.1.3.1.
The data applied, including references for citation, are presented in Table 4.16.
Data Storage level 1
4.Consistency DS.1.4.1 The origin of external data has to be pre-served whenever possible without explicit arguments (referring to other PMs).
Data Storage level 1
6.Robustness DS.1.6.1 Explicit agreements between the external institution holding the data and NERI about the condition of delivery.
Data Storage level 1
7.Transparency DS.1.7.1 Summary of each dataset including the reasoning for selecting the specific dataset.
Data Storage level 1
7.Transparency DS.1.7.3 References for citation for any external dataset have to be available for any single value in any dataset.
197
The applied data including external contacts are presented in Table 4.16.
The uncertainty assessment has been performed on Tier 1 level, assum-ing a normal distribution of activity data as well as emission data, by ap-plication of default uncertainty factors. Therefore, no considerations re-garding distribution or type of variability have been performed.
See DP.1.1.2.
The applied methodologies are in line with the international guidelines issued by the IPCC combined with national adjustments. The degree of fulfilment of the required methodology has been documented in an in-ternal note (Kyoto note).
The emission factors applied are mostly based on chemical equations and are, therefore, in accordance with the default EFs. E.g. for produc-tion of nitric acid, where the emission factor is dependent on process conditions, a comparison has been made to the default EF listed in the guideline. E.g. for the deviation of the emission factor for calcination in the cement process, an explanation has been developed in cooperation with the company.
See DP.1.1.3
This issue will be investigated further.
Data Storage level 1
7.Transparency DS.1.7.4 Listing of external contacts for every data-set.
Data
Processing level 1
1. Accuracy DP.1.1.1 Uncertainty assessment for every data source as input to Data Storage level 2 in relation to type of variability (distribution as: normal, log normal or other type of variabil-ity).
Data
Processing level 1
1. Accuracy DP.1.1.2 Uncertainty assessment for every data source as input to Data Storage level 2 in relation to scale of variability (size of varia-tion intervals).
Data
Processing level 1
1. Accuracy DP.1.1.3 Evaluation of the methodological approach using international guidelines.
Data
Processing level 1
1. Accuracy DP.1.1.4 Verification of calculation results using guideline values.
Data
Processing level 1
2.Comparability DP.1.2.1 The inventory calculation has to follow the international guidelines suggested by UNFCCC and IPCC.
Data
Processing level 1
3.Completeness DP.1.3.1 Assessment of the most important quanti-tative knowledge which is which is lacking.
198
Accessibility to critical company-specific information will be established as a consequence of the formal agreement with the Danish Energy Au-thority concerning data compiled in relation to the EU-ETS.
Recalculations are described in the NIR. A manual log is included in the tool used for data processing at Data Processing level 2. This log also in-cludes changes on Data Processing level 1.
The sector report for industry (in prep.) presents an independent exam-ple of the calculations to ensure the correctness of every data manipula-tion.
The calculations are verified by checking the time-series.
A methodology to verify calculation of results using other measures will be developed.
A methodology to check the correctness between external data sources and the databases at storage level 2 will be developed.
The calculation principles and equations are based on the methodology presented by the IPCC. A detailed description can be found in the sector report for industry (in prep.).
Data
Processing level 1
3.Completeness DP.1.3.2 Assessment of the most important cases where access is lacking with regard to critical data sources that could improve quantitative knowledge.
Data
Processing level 1
4.Consistency DP.1.4.1 In order to keep consistency at a high level, an explicit description of the activities needs to accompany any change in the calculation procedure.
Data
Processing level 1
5.Correctness DP.1.5.1 Show at least once, by independent calcu-lation, the correctness of every data ma-nipulation.
Data
Processing level 1
5.Correctness DP.1.5.2 Verification of calculation results using time-series.
Data
Processing level 1
5.Correctness DP.1.5.3 Verification of calculation results using other measures.
Data
Processing level 1
5.Correctness DP.1.5.4 Shows one-to-one correctness between external data sources and the databases at Data Storage level 2.
Data
Processing level 1
7.Transparency DP.1.7.1 The calculation principle and equations used must be described.
Data
Processing level 1
7.Transparency DP.1.7.2 The theoretical reasoning for all methods must be described.
199
The theoretical reasoning for choice or development of methods is de-scribed in detail in the sector report for industry (in prep.).
The assumptions used in the different methods are described in the sec-tor report for industry (in prep.) and also included in the present report. An explicit list of assumptions will be developed in the coming sector report.
Explicit references from the data processing to each dataset can be found in the sector report for industry (in prep.).
A manual log is included in the tool used for data processing at data level 2. This log also includes changes on Data Processing level 2. A de-tailed log will be developed in the sector report for industry (in prep.).
The sector report for industry (in prep.) presents the connection between the datasets on Data Storage level 1 and Data Processing level 2. Indi-vidual calculations are used to check the output of the data processing tool used at Data Processing level 2.
Danish Energy Authority (DEA), 2004: Anders Baunehøj Hansen, per-sonal communication, 15 December 2004.
Data
Processing level 1
7.Transparency DP.1.7.3 Explicit listing of assumptions behind all methods.
Data
Processing level 1
7.Transparency DP.1.7.4 Clear reference to data set at Data Storage level 1.
Data
Processing level 1
7.Transparency DP.1.7.5 A manual log to collect information about recalculations.
Data
Processing level 2
5.Correctness DS.2.5.1 Documentation of a correct connection between all data types at level 2 to data at level 1.
Data
Processing level 2
5.Correctness DS.2.5.2 Check if a correct data import to level 2 has been made.
200
Danish Environmental Protection Agency, 2007: Ozone-depleting sub-stances and the greenhouse gases HFCs, PFCs and SF6. Danish consump-tion and emissions, 2005. Environmental Project no 1168.
EMEP/CORINAIR, 2004: Emission Inventory Guidebook 3rd edition, prepared by the UNECE/EMEP Task Force on Emissions Inventories and Projections, 2004 update. Available at http://reports.eea.eu.int/ EMEPCORINAIR4/en (15-04-2007).
Energinet.dk, 2006: Baggrundsrapport til Miljøberetning 2006 (in Da-nish).
Faxe Kalk, 2003: Diverse produktblade (in Danish).
Haldor Topsøe 2006: Miljøredegørelse for katalysatorfabrikken 2005 (10. regnskabsår); incl. 1996-2004 (in Danish).
Hjelmar, O. & Hansen, J.B., 2002: Restprodukter fra røggasrensning på affaldsforbrændingsanlæg. Nyttiggørelse eller deponering? DHI - Insti-tut for Vand & Miljø. Kursusmateriale fra kurset Røggasrensning 2002. IDA, Brændsels- og Energiteknisk Selskab (in Danish).
Illerup, J.B., Geertinger A.M., Hoffmann, L. & Christiansen, K., 1999: Emissionsfaktorer for tungmetaller 1990 - 1996. Faglig rapport fra DMU, nr. 301. Miljø- og Energiministeriet, Danmarks Miljøundersøgelse (in Danish).
IPCC, 1997: Revised 1996 IPCC Guidelines for National Greenhouse Gas Inventories. Available at http://www.ipcc-nggip.iges.or.jp/public/gl/ invs6.htm (15-04-2007).
IPCC, 2000: Good Practice Guidance and Uncertainty Management in National Greenhouse Gas Inventories. Available at http://www.ipcc-nggip.iges.or.jp/public/gp/english/ (15-04-2007).
Miljøstyrelsen, 2006: Affaldsstatistik 2005. Orientering fra Miljøstyrelsen Nr. 6 2006 (In Danish).
Nielsen, M. & Illerup, J.B., 2003: Emissionsfaktorer og emissionsopgørel-se for decentral kraftvarme. Eltra PSO projekt 3141. Kortlægning af emis-sioner fra decentrale kraftvarmeværker. Delrapport 6. Danmarks Miljø-undersøgelser. 116 s. Faglig rapport fra DMU nr. 442. http://www-.dmu.dk/udgivelser/ (in Danish).
Rexam Glass Holmegaard, 2006: Grønt regnskab for Rexam Glass Hol-megaard A/S 2005, CVR nr. 18445042; incl. 1996/97-2004 (in Danish).
Rockwool, 2006: Miljøredegørelse 2005 for fabrikkerne i Hedehusene, Vamdrup og Øster Doense; incl. 1996-2004 (in Danish).
Saint-Gobain Isover, 2006: Miljø- og energiredegørelse 2005; incl. 1996-2004 (in Danish).
201
Statistics Denmark, 2006: Statbank Denmark. Available at www.statba-nk.dk
Stålvalseværket, 2002: Grønt regnskab og miljøredegørelse 2001. Det Danske Stålvalseværk A/S; incl. 1992, 1994-2000 (in Danish).
Use of solvents and other organic compounds in industrial processes and households are important sources of evaporation of non-methane vola-tile hydrocarbons (NMVOC), and are related to the source categories Paint application (CRF sector 3A), Degreasing and dry cleaning (CRF sector 3B), Chemical products, manufacture and processing (CRF sector 3C) and Other (CRF sector 3D). In this section a new methodology for the Danish NMVOC emission inventory is presented and the results for the period 1995 – 2005 are summarised. The method is based on a chemi-cal approach, and this implies that the SNAP category system is not di-rectly applicable. Instead emissions will be related to specific chemicals, products, industrial sectors and households and to the CRF sectors men-tioned before.
Table 5.1 and Figure 5.1 show the emissions of chemicals from 1985 to 2005, where the used amounts of single chemicals have been assigned to specific products and CRF sectors. The methodological approach for finding emissions in the period 1995 - 2005 is described in the following section. A linear extrapolation is made for the period 1985 – 1995. A gen-eral decrease is seen throughout the sectors, however, with an increase in total emissions during the latest three reported years. Table 5.2 shows the used amounts of chemicals for the same period. Table 5.1 is derived from Table 5.2 by applying emission factors relevant to individual chemicals and production or use activities. Table 5.3 showing the used amount of products is derived from Table 5.2, by assessing the amount of chemicals that is comprised within products belonging to each of the four source categories. The conversion factors are very rough estimates, and more thorough investigations are needed in order to quantify the used amount of products more accurately.
In Table 5.4 the emission for 2005 is split into individual chemicals. Pro-pane and butane are main contributors, which can be attributed to pro-pellants in spraying cans. Turpentine is defined as a mixture of stoddard solvent and solvent naphtha. For each chemical the emission factors are based on rough estimates from SFT (1994). High emission factors are as-sumed for use of chemicals (products) and lower factors for industrial production processes.
���������� Emissions of chemicals in Gg pr year. The methodological approach for finding emissions in the period 1995 – 2005 is described in the text, and a linear extrapolation is made for 1985 – 1995. Fig-ures can be seen in Table 5.1
�� ����� Chemicals with highest emissions 2005
Chemical
Emissions 2005
(kg/year)
Turpentine (solvent naphtha and Stoddard solvent) 5339412
propane 5000000
butane 5000000
methanol 4350997
aminooxygengroups 3005451
ethanol 2024512
naphthalene 2022969
pentane 1995706
glycerol 1934542
acetone 1204261
propylenglycol 784147
ethylenglycol 652206
glycolethers 579784
formaldehyde 484883
propylalcohol 466252
butanone 441071
xylene 280520
1-butanol 248607
methylbromide 234033
toluene 214520
toluendiisocyanate 195034
phenol 124694
acyclic monoamines 90468
methyl methacrylate 85784
butanoles 50571
dioctylphthalate 50307
styrene 43502
tetrachloroethylene 32674
triethylamine 6481
diethylenglycol 5725
diamines 83
205
������ ������������������������������������
Estimation of uncertainty is based on the Tier 1 methodology in IPCC Good Practice Guidance. Input to the uncertainty estimates are shown in Table 5.5.
Overall uncertainty in 2005: 32.5%
Trend uncertainty: 26.1%
206
��������� Emission uncertainties for solvents (NMVOCs). Only combined uncertainties are applied as uncertainties are not differentiated into activity data and emission factors in the Emis-sion Inventory Guidebook. Furthermore uncertainties are only stated for the total emissions. This uncertainty is distributed equally on activity data and emission factors.
Source Activity SNAP code Activity Base year emission 2005 emissions Activity data uncertainty Emission factor uncertainty Combined uncertainty
Input data (Mg) Input data (Mg) Input data (%) Input data (%) (%)
Paint application 60101
60102
60103
60104
60105
60106
60107
60108
60109
Manufacture of Automobiles
Car Repairing
Construction and Buildings
Domestic Use
Coil Coating
Boat Building
Wood
Other Industrial Paint Application
Other Non-Industrial Paint Application 18454 13579
Not estimated Not estimated Not estimated
Degreasing and dry
cleaning
60201
60202
60203
60204
Metal Degreasing
Dry Cleaning
Electronic Components Manufacturing
Other Industrial Dry Cleaning 7658 5240
Not estimated Not estimated Not estimated
Chemical products,
manufacturing and
processing
60301
60302
60303
60304
60305
60306
60307
60308
60309
60310
60311
60312
60313
60314
Polyester Processing
Polyvinylchloride Processing
Polyurethane Foam Processing
Polystyrene Foam Processing
Rubber Processing
Pharmaceutical Products Manufacturing
Paints Manufacturing
Inks Manufacturing
Glues Manufacturing
Asphalt Blowing
Adhesive, Magnetic Tapes, Film and Photographs Manufacturing
Textile Finishing
Leather Tanning
Other 1221 2460
Not estimated Not estimated Not estimated
Other 60401
60402
60403
60404
60405
60406
60407
60408
60409
60411
60412
Glass Wool Enduction
Mineral Wool Enduction
Printing Industry
Fat, Edible and Non-Edible Oil Extraction
Application of Glues and Adhesives
Preservation of Wood
Underseal Treatment and Conservation of Vehicles
Domestic Solvent Use (Other Than Paint Application)
Vehicles Dewaxing
Domestic Use of Pharmaceutical Products
Other(Preservation of Seeds, ...) 18638 15671
Not estimated Not estimated Not estimated
Total 60000 Solvent and Other Product Use 45971 36949 46 46 65
207
������ ����� ������������
The emissions of Non-Methane Volatile Organic Compounds (NMVOC) from industrial use and production processes and house-hold use in Denmark have been assessed. Until 2002 the NMVOC in-ventory in Denmark was based on questionnaires and interviews with different industries, regarding emissions from specific activities, such as lacquering, painting impregnation etc. However, this approach im-plies large uncertainties due to the diverse nature of many solvent-using processes. For example, it is inaccurate to use emission factors derived from one printwork in an analogue printwork, since the type and combination of inks may vary considerably. Furthermore the em-ployment of abatement techniques will result in loss of validity of es-timated emission factors.
A new approach has been introduced, focusing on single chemicals in-stead of activities. This will lead to a clearer picture of the influence from each specific chemical, which will enable a more detailed differ-entiation on products and the influence of product use on emissions.
The procedure is to quantify the use of the chemicals and estimate the fraction of the chemicals that is emitted as a consequence of use. Mass balances are simple and functional methods for calculating the use and emissions of chemicals
where “hold up” is the difference in the amount in stock in the begin-ning and at the end of the year of inventory.
A mass balance can be made for single substances or groups of sub-stances, and the total amount of emitted chemical is obtained by sum-ming up the individual contributions. It is important to perform an in-depth investigation in order to include all relevant emissions from the large amount of chemicals. The method for a single chemical approach is shown in Figure 5.2.
��������� Methodological flow in a chemical based emission inventory.
chemical
product product
activity activity activity activity activity
air
activity
soil water waste etc......
etc......
208
The tasks in a chemical focused approach are
• Definition of chemicals to be included • Quantification of use amounts from Eq.1 • Quantification of emission factors for each chemical In principle all chemicals that can be classified as NMVOC must be in-cluded in the analysis, which implies that it is essential to have an ex-plicit definition of NMVOC. The definition of NMVOC is, however, not consistent; In the EMEP-guidelines for calculation and reporting of emissions, NMVOC is defined as ”all hydrocarbons and hydrocarbons where hydrogen atoms are partly or fully replaced by other atoms, e.g. S, N, O, halogens, which are volatile under ambient air conditions, ex-cluding CO, CO2, CH4, CFCs and halons”. The amount of chemicals that fulfil these criteria is large and a list of 650 single chemicals and a few chemical groups described in ”National Atmospheric Emission Inventory”, cf. Annex 3.F, is used. It is probable that the major part will be insignificant in a mass balance, but it is not correct to exclude any chemicals before a more detailed investigation has been made. It is important to be aware that some chemicals are comprised in products and will not be found as separate chemicals in databases, e.g. di-ethylhexyl –phthalate (DEHP), which is the predominant softener in PVC. In order to include these chemicals the product use must be found and the amount of chemicals in the product must be estimated. It is important to distinguish the amount of chemicals that enters the mass balance as pure chemical and the amount that is associated to a product, in order not to overestimate the use.
Production, import and export figures are extracted from Statistics Denmark, from which a list of 427 single chemicals, a few groups and products is generated. For each of these a ��� amount in tonnes pr. year (from 1995 to 2004) is calculated. It is found that 44 different NMVOCs comprise over 95 % of the total use, and it is these 44 chemi-cals that are investigated further.
In the Nordic SPIN database (Substances in Preparations in Nordic Countries) information for industrial use categories and products specified for individual chemicals, according to the NACE coding sys-tem is available. This information is used to distribute the ��� amounts of individual chemicals to specific products and activities. The product amounts are then distributed to the CRF sectors 3A – 3D.
Emission factors, cf. Eq. 2, are obtained from regulators or the industry and can be provided on a site by site basis or as a single total for whole sectors. Emission factors can be related to production processes and to use. In production processes the emissions of solvents typically are low and in use it is often the case that the entire fraction of chemical in the product will be emitted to the atmosphere. Each chemical will therefore be associated with two emission factors, one for production processes and one for use.
Outputs from the inventory are
• a list where the 44 most predominant NMVOCs are ranked accord-ing to emissions to air,
209
• specification of emissions from industrial sectors and from house-holds,
• contribution from each NMVOC to emissions from industrial sec-tors and households,
• yearly trend in NMVOC emissions, expressed as total NMVOC and single chemical, and specified in industrial sectors and households.
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Important uncertainty issues related to the mass-balance approach are
(i) Identification of chemicals that qualify as NMVOCs. The definition is vague, and no approved list of agreed NMVOCs is available. Al-though a tentative list of 650 chemicals from the ”National Atmos-pheric Emission Inventory” has been used, it is possible that relevant chemicals are not included.
(ii) Collection of data for quantifying production, import and export of single chemicals and products where the chemicals are comprised. For some chemicals no data are available in Statistics Denmark. This can be due to confidentiality or that the amount of chemicals must be de-rived from products wherein they are comprised. For other chemicals the amount is the sum of the single chemicals ��� product(s) where they are included. The data available in Statistics Denmark is obtained from Danish Customs & Tax Authorities and they have not been veri-fied in this assessment.
(iii) Distribution of chemicals on products, activities, sectors and households. The present approach is based on amounts of single chemicals. To differentiate the amounts into industrial sectors it is nec-essary to identify and quantify the associated products and activities and assign these to the industrial sectors and households. No direct link is available between the amounts of chemicals and products or ac-tivities. From the Nordic SPIN database it is possible to make a relative quantification of products and activities used in industry, and com-bined with estimates and expert judgement these products and activi-ties are differentiated into sectors. The contribution from households is also based on estimates. If the household contribution is set too low, the emission from industrial sectors will be too high and vice versa. This is due to the fact that the total amount of chemical is constant. A change in distribution of chemicals between industrial sectors and households will, however, affect the total emissions, as different emis-sion factors are applied in industry and households, respectively.
A number of activities are assigned as “other”, i.e. activities that can not be related to the comprised source categories. This assignment is based on expert judgement but it is possible that the assigned amount of chemicals may more correctly be included in other sectors. More de-tailed information from the industrial sectors is continuously being implemented.
(iv) Rough estimates and assumed emission factors are used for many compounds. For some compounds more reliable information has been obtained from the literature and from communication with industrial
210
sectors. In some cases it is more appropriate to define emission factors for sector specific activities rather than for the individual chemicals.
A quantitative measure of the uncertainty has not been assessed. Sin-gle values have been used for emission factors and activity distribu-tion ratios etc. In order to perform a stochastic evaluation more infor-mation is needed.
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�� ����� External and internal data
The QA/QC procedure is outlined in section 1.6. In general, Critical Control Points (CCP) have been defined as elements or actions which need to be addressed in order to fulfil the quality objectives. The CCPs have to be based on clear measurable factors, expressed through a number of Points for Measuring (PM). In section 1.6 the list of PMs are listed.
The sources of data described in the methodology section and in DS.1.2.1 and DS.1.3.1 are used in this inventory. It is the accuracy of these data that define the uncertainty of the inventory calculations. Any data value obtained from Statistics Denmark and SPIN is given as a single point estimate and no probability range or uncertainty is asso-ciated with this value. The emission factors stated in the Norwegian solvent inventory are rough estimates, given either as single values or as ranges, for groups of chemicals. Information from reports is some-times given in ranges.
No uncertainty levels are quantified for the external data.
File or folder name Description AD or Emf. Reference Contact(s) Data agreement/ Comment
“Emissioner NMVOC” folder Production, import and export data from Statistics Denmark
Activity data Statistics Denmark Patrik Fauser
NMVOC emissions.xls Calculations, emissionfactors, SPIN data. For industrial branches
Activity data and emissionfactors
Statistics Denmark,
SPIN, reports, personal communication
Patrik Fauser
Use Category National.xls Calculations, emissionfactors, SPIN data. For CRF
Activity data and emissionfactors
Statistics Denmark, SPIN, reports, personal communication
Patrik Fauser
Data Storage
level 1
1. Accuracy DS.1.1.1 General level of uncertainty for every data set including the reasoning for the specific values
Data Storage
level 1
1. Accuracy DS.1.1.2 Quantification of the uncertainty level of every single data value including the rea-soning for the specific values.
211
1) Production and import/export data from Statistics Denmark for single chemicals can be directly compared with data from Eurostat for other countries. This has been done for a few chosen chemicals and countries. Furthermore chosen Danish data from Eurostat have been validated with data from Statistics Denmark in order to check the con-sistency in data transfer from national to international databases.
2) Use categories for chemicals in products are found from Nordic SPIN database. Data for all Nordic countries are available and re-ported uniformly. For chosen chemicals a comparison of chemical amounts and use has been made between countries.
3) The Norwegian solvent inventory has been used for input on meth-odological issues and for estimates on emission factors. The methodol-ogy has been adjusted for Danish conditions, while many emission factors are identical to the emission factors suggested in the Norwe-gian inventory.
A number of external date sources form the basis for calculating emis-sions of single chemicals. The general methodology in the emission in-ventory is described above.
1) Statistics Denmark. Statistics Denmark is used as the main database for collecting data on production, import and export of single chemi-cals, chemical groups and for some products. In order to obtain a uni-form and unique set of data, it is crucial that the data for e.g. produc-tion of single chemicals is in the same reporting format and from the same source. The amount of data is very comprehensive and is linked with the data present in Eurostat. The database covers all sectors and is regarded as complete on a national level.
2) Nordic SPIN database (Substances in Preparations in Nordic Coun-tries). SPIN provides data on the use of chemical substances in Norway, Sweden, Denmark and Finland . It is financed by the Nordic Council of Ministers, Chemical group and the data is supplied by the product registries of the contributing countries. The Danish product register (PROBAS) is a joint register for the Danish Working Environ-ment Authority and the Danish EPA and comprises a large number of chemicals and products. The information is obtained from registration according to the Danish EPA rules and from scientific studies and sur-veys and other relevant sources. The product register is the most com-prehensive collection of chemical data in products for Denmark, and the availability of data from the other Nordic countries enables an in-ter-country comparison. For each chemical the data is reported in a uniform way, which enhances comparability, transparency and consis-tency.
Data Storage
level 1
2. Comparability DS.1.2.1 Comparability of the data values with simi-lar data from other countries, which are comparable with Denmark and evaluation of the discrepancy.
Data Storage
level 1
3.Completeness DS.1.3.1 Documentation showing that all possible national data sources are included by set-ting up the reasoning for the selection of data sets
212
3) Reports from and personal contacts with industrial branches. It is fundamental to have information from the industrial branches that have direct contact with the activities, i.e. chemicals and products of interest. The information can be in the form of personal communica-tion, but also reported surveys are of great importance. In contrast to the more generic approach of collecting information from large data-bases, the expert information from industrial branches may give valu-able information on specific chemicals and/or products. By consider-ing both sources a verification and optimum reliability and accuracy is obtained. The propane and butane use, as described above, is a good example of the importance of industrial branch information.
4) The present inventory procedure builds partly on information from the previous Danish solvent emission inventory, which is based on questionnaires to industrial branches. Furthermore the Norwegian solvent inventory has been used for input on methodological issues and for estimates on emission factors.
Data are predominantly extracted from the internet (Statistics Den-mark and SPIN). These are saved as original copies in their original form, cf. Table 5.6. Specific information from industries and experts are saved as e-mails and reports.
As stated in DS.1.4.1 most data is obtained from the internet. No ex-plicit agreements have been made with external institutions.
See DS.1.3.1.
See Table 5.6.
See Table 5.6.
Data Storage
level 1
4.Consistency DS.1.4.1 The origin of external data has to be pre-served whenever possible without explicit arguments (referring to other PM’s)
Data Storage
level 1
6.Robustness DS.1.6.1 Explicit agreements between the external institution of data delivery and NERI about the condition of delivery
Data Storage
level 1
7.Transparency DS.1.7.1 Summary of each data set including the reasoning for selecting the specific data set
Data Storage
level 1
7.Transparency DS.1.7.3 References for citation for any external data set have to be available for any single number in any data set.
Data Storage
level 1
7.Transparency DS.1.7.4 Listing of external contacts to every data set
213
Tier1 assumes normal distribution of activity data and emission fac-tors.
In the Emission Inventory Guidebook uncertainty estimates for the fi-nal emission calculations are given for the associated SNAP codes. These codes and uncertainty estimates are shown in Table 5.5.
The methodological approach described in section 5.2.3 is based on the detailed methodology as outlined in the Emission Inventory Guide-book.
No guideline values are stated for Denmark in the Emission Inventory Guidebook.
See DP.1.1.3 and DS.1.3.1.
In “Uncertainties and time-series consistency” section 5.2.4 important uncertainty issues related to missing quantitative knowledge is stated. To summarise; (i) identification and inclusion of all relevant chemicals. (ii) Collection of data for quantifying production, import and export of single chemicals. (iii) Distribution of chemicals on products, activities, sectors and households. (iv) Emission factors for single chemicals, products and industrial and household activities.
The issues are referring to DP.1.3.1: (i) Identification of chemicals that qualify as NMVOCs. The definition is vague, and no approved list of
Data Processing
level 1
1. Accuracy DP.1.1.1 Uncertainty assessment for every data source as input to Data Storage level 2 in relation to type of variability (Distribution as: normal, log normal or other type of variabil-ity)
Data Processing
level 1
1. Accuracy DP.1.1.2 Uncertainty assessment for every data source as input to Data Storage level 2 in relation to scale of variability (size of varia-tion intervals)
Data Processing
level 1
1. Accuracy DP.1.1.3 Evaluation of the methodological approach using international guidelines
Data Processing
level 1
1. Accuracy DP.1.1.4 Verification of calculation results using guideline values
Data Processing
level 1
2.Comparability DP.1.2.1 The inventory calculation has to follow the international guidelines suggested by UNFCCC and IPCC.
Data Processing
level 1
3.Completeness DP.1.3.1 Assessment of the most important missing quantitative knowledge
Data Processing
level 1
3.Completeness DP.1.3.2 Assessment of the most important missing accessibility to critical data sources that could improve quantitative knowledge
214
agreed NMVOCs is available. Although a tentative list of 650 chemi-cals from the ”National Atmospheric Emission Inventory” has been used, it is possible that relevant chemicals are not included. (ii) For some chemicals no data are available in Statistics Denmark. This can be due to confidentiality or that the amount of chemicals must be de-rived from products wherein they are comprised. (iii) No direct link is available between the amounts of chemicals and products or activities. From the Nordic SPIN database it is possible to make a relative quanti-fication of products and activities used in industry, and combined with estimates and expert judgement these products and activities are differentiated into sectors. More detailed information from the indus-trial sectors is still required. (iv) For many industrial and household activities involving solvent containing products no estimates on emis-sion factors are available. Large variations occur between industry and product groups. And given the large number of chemicals more spe-cific knowledge regarding industrial processes and consumption is needed.
Any changes in calculation procedures are noted for each years inven-tory.
Calculations performed by IIASA using RAINS codes, which are based on a different methodological approach gives total emission values that are similar to the emissions found in the present approach.
No detailed guidelines or calculations are accessible for time-series. These are therefore not used in verification.
No other measures are used for verification.
The transfer of emission data from level 1, storage and processing, to data storage level 2 is manually checked.
Data Processing
level 1
4.Consistency DP.1.4.1 In order to keep consistency at a higher level an explicit description of the activities needs to accompany any change in the calculation procedure
Data Processing
level 1
5.Correctness DP.1.5.1 Shows at least once by independent calcu-lation the correctness of every data manipu-lation
Data Processing
level 1
5.Correctness DP.1.5.2 Verification of calculation results using time-series
Data Processing
level 1
5.Correctness DP.1.5.3 Verification of calculation results using other measures
Data Processing
level 1
5.Correctness DP.1.5.4 Shows one to one correctness between external data sources and the data bases at Data Storage level 2
215
See methodological approach described in section 5.2.3
See methodological approach described in section 5.2.3
See methodological approach described in section 5.2.3
See Table 5.6
Any changes in calculation procedures and methods are noted for each year’s inventory.
See DP.1.5.4.
See DP.1.5.4.
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The previous method was based on results from an agreement be-tween the Danish Industry and the Danish Environmental Protection Agency (EPA). The emissions from various industries were reported to the Danish EPA. The reporting was not annual and linear interpolation was used between the reporting years. It is important to notice that not all use of solvents was included in this agreement and no activity data were available. It is not possible to perform direct comparison of methodologies or to make corrections to the previous method, due to the fundamental differences in structure. But an increase in total emis-sions was expected due to the more comprehensive list of chemicals.
Improvements and additions are continuously being implemented in the new approach, due to the comprehensiveness and complexity of
Data Processing
level 1
7.Transparency DP.1.7.1 The calculation principle and equations used must be described
Data Processing
level 1
7.Transparency DP.1.7.2 The theoretical reasoning for all methods must be described
Data Processing
level 1
7.Transparency DP.1.7.3 Explicit listing of assumptions behind all methods
Data Processing
level 1
7.Transparency DP.1.7.4 Clear reference to data set at Data Storage level 1
Data Processing
level 1
7.Transparency DP.1.7.5 A manual log to collect information about recalculations
Data Storage level 2
5.Correctness DS.2.5.1 Documentation of a correct connection between all data type at level 2 to data at level 1
Data Storage level 2
5.Correctness DS.2.5.2 Check if a correct data import to level 2 has been made
216
the use and application of solvents in industries and households. The improvements in the 2005 reporting include revisions of the following
• More detailed information concerning chemical patterns and amounts have been made for four industrial branches, comprising approximately 20% of the total emissions. The branches are plastic industry, graphic industry, auto repairers and colour and lacquer industry.
• Use amounts and emission factors have been refined for pentane and styrene used especially in the plastic industry.
• The group of glycolethers has been rearranged and comprises more single chemical compounds. The distribution of glycolethers in in-dustrial branches has been revised, and the emission factors have been changed. E.g. for use in dry cleaning an emission factor of 0.0001 is used.
• Tetrachloroethylene has been removed from use in auto repairers and others, and has been assigned to dry cleaners and metal indus-try. Emission factor of 0.0001 is assigned for use in dry cleaning as a recovery of 99.99% of solvent used is stated in the literature.
• Some product categories (as defined in SPIN database) have been transferred from degreasing to paints category. This implies that the used amounts of products in Table 5.3 has increased compared to the latest inventory, because the amount of chemical in a product from the paints category is lower than the amount of chemical in a product from the degreasing category.
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In line with the latest refinements of four industrial branches, more branches will be addressed for further adjustments in the following inventory. More detailed information will be obtained for selected in-dustries with respects to used products and chemicals and emission factors related to the activities.
!����������
Statistics Denmark, http://www.dst.dk/HomeUK.aspx
SPIN on the Internet. Substances in Preparations in Nordic Countries, http://www.spin2000.net/spin.html
EMEP/CORINAIR, 2004: Emission Inventory Guidebook 3rd edition, prepared by the UNECE/EMEP Task Force on Emissions Inventories and Projections, 2004 update. Available at http://reports.eea.e-u.int/EMEPCORINAIR3/en (15-04-2007)
Solvent Balance for Norway, 1994. Statens Forurensningstilsyn, rap-port 95:02
The emission of greenhouse gases from agricultural activities includes the CH4 emission from enteric fermentation and manure management, and the N2O emission from manure management and agricultural soils. The emissions are reported in CRF Tables 4.A, 4.B(a), 4.B(b) and 4.D. Furthermore, the emission of non-methane volatile organic com-pounds (NMVOC) from agricultural soils is given in CRF Table 4s2. CO2 emissions from agricultural soils are ncluded in the LULUCF sec-tor.
Emission from rice production, burning of savannas and crop residues does not occur in Denmark and the CRF Tables 4.C, 4E and 4.F have, consequently, not been completed. Burning of plant residue has been prohibited since 1990 and may only take place in connection with con-tinuous cultivation of seed grass. It is assumed that the emission is in-significant and, hence, not included in the emission inventory.
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In CO2 equivalents, the agricultural sector (with LULUCF) contributes with 16% of the overall greenhouse gas emission (GHG) in 2005. Next to the energy sector, the agricultural sector is the largest source of GHG emission in Denmark. The major part of the emission is related to livestock production, which in Denmark is dominated by the pro-duction of cattle and pigs. Given in CO2 equivalents, the N2O emission contributed with 63% of the total GHG emission from the agricultural sector and CH4 contributed with the remaining 37% in 2005.
From 1990 to 2005, the emissions decreased from 13.0 Gg CO2 eqv. to 9.9 Gg CO2 eqv., which corresponds to a 24% reduction (Table 6.1). Since the previous reporting, there have been some small changes. The change has affected the total emission 1990 – 2005 by less than 1% (Sec-tion 6.8).
218
�� ����� Emission of GHG in the agricultural sector in Denmark 1990 – 2005
Figure 6.1 shows the distribution of the greenhouse gas emission across the main agricultural sources. The total N2O emission from 1990-2005 has decreased by 31%. The decrease in total emissions can largely be attributed to the decrease in N2O emissions from agricul-tural soils. This reduction is due to a proactive national environmental policy over the last twenty years. The environmental policy has intro-duced a series of measures to prevent loss of nitrogen from agricul-tural soil to the aquatic environment. The measures include improve-ments to the utilisation of nitrogen in manure, a ban on manure appli-cation during autumn and winter, increasing area with winter-green fields to catch nitrogen, a maximum number of animals per hectare and maximum nitrogen application rates for agricultural crops. The main part of the emission from the agricultural sector is related to live-stock production. An active environmental policy has brought about a decrease in the N-excretion, a decrease of emission per produced ani-mal and a fall in use of mineral fertilizer, which all has reduced the overall GHG emission.
From 1990 to 2005, only a slight reduction in the total CH4 emission has occurred. The emission from enteric fermentation has decreased due to a reduction in the number of cattle. On the other hand, the emission from manure management has increased due to a change towards greater use of slurry-based stable systems, which have a higher emission factor than systems with solid manure. By coinci-dence, the decrease and the increase almost balance each other out and the total CH4 emission from 1990 to 2005 has decreased by 9%.
Total (Gg CO2-eqv.) 10616 10558 10239 10089 10039 9880
219
0
2000
4000
6000
8000
10000
12000
14000
16000
18000
20000
1985
1987
1989
1991
1993
1995
1997
1999
2001
2003
2005
Gg
CO
2 e
qv
�
N2O - Agricultural soils
N2O - manure management
CH4 - Enteric fermentation
CH4 - Manure management
���������� Danish greenhouse gas emissions 1990 – 2005
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The calculations of the emissions are based on methods described in the IPCC Reference Manual (IPCC, 1997) and the Good Practice Guid-ance (IPCC, 2000).
Activity data and emission factors are collected and discussed in coop-eration with specialists and researchers in various institutes, such as the Faculty of Agricultural Sciences – Aarhus University, Statistics Denmark, the Danish Agricultural Advisory Centre, the Danish Plant Directorate and the Danish Environmental Protection Agency. In this way, both data and methods will be evaluated continually, according to the latest knowledge and information. National Environmental Re-search Institute has established data agreements with the institutes and organisations to assure that the necessary data is available to pre-pare the emission inventory on time.
220
�� ���� List of institutes involved in the emission inventory for the agricultural sector.
The emissions from the agricultural sector are calculated in a compre-hensive agricultural model complex called DIEMA (Danish Integrated Emission Model for Agriculture). This model complex, as shown in Figure 6.2, is implemented in great detail and is used to cover emis-sions of ammonia, particulate matter and greenhouse gases. Thus, there is a direct coherence between the ammonia emission and the emission of N2O. A more detailed description has been published (Mikkelsen et al. 2006).
References Link Abbreviation Data / information
National Environmental Research Institute, University of Aarhus
www.dmu.dk
NERI - reporting
- data collecting
Statistics Denmark
– Agricultural Statistics
www.dst.dk
DS
- No. of animal
- milk yield
- slaughter data
- land use
- crop production
- crop yield
Faculty of Agricultural Sciences,
University of Aarhus
www.agrsci.dk
FAS - N-excretion
- feeding situation
- growth
- N-fixed crops
- crop residue
- N-leaching/runoff
- NH3 emissions factor
The Danish Agricultural Advisory Centre
www.lr.dk
AAC - stable type
- grassing situation
- manure application time and methods
Danish Environmental Protection Agency www.mst.dk
EPA - sewage sludge used as fertiliser
- industrial waste used as fertiliser
The Danish Plant Directorate www.plantedi-rektoratet.dk
PD - synthetic fertiliser (consumption and type)
- stable type (from 2005)
The Danish Energy Authority www.ens.dk DEA - manure used in biogas plants
221
��������� DIEMA – Danish Integrated Emission Model for Agriculture
%��.������� ��������������������������-stable type - application - grassing
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.����������-fertiliser - stable type (for 2005)
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222
The DIEMA model complex is build up as a number of spreadsheets, where data is linked between the sheets. The main part of the emission is related to livestock production. In short, the emission from livestock production is based on information concerning the number of animals, the distribution of animals according to stable type and final informa-tion on feed consumption and excretion.
DIEMA operates with 30 different livestock categories, according to livestock category, weight class and age. These categories are subdi-vided into stable type and manure type, which results in around 100 different combinations of livestock subcategories and stable types. For each of these combinations, information on e.g. feed intake, digestibil-ity, excretion and methane conversion factors is attached. The emis-sion is calculated from each of these subcategories and then aggre-gated in accordance with the IPCC livestock categories given in the CRF.
Table 6.3 shows an example of subcategories for cattle and swine.
�� ����� Subcategories including in category of Dairy Cattle, Non-Dairy Cattle and Swine
1 For all subcategories, large breed and jersey cattle are distinguished from each other
It is important to point out that changes over the years, both to the to-tal emission and the implied emission factor, are not only a result of changes in the numbers of animals, but also depend on changes in the allocation of subcategories, changes in feed consumption and changes in stable type.
Number of animals: Livestock production is primarily based on the agricultural census from Statistics Denmark. The emission from slaughter pigs and poultry is based on slaughter data. Approximate numbers of horses, goats and sheep on small farms are added to the number in the Agricultural Statistics, in agreement with the Danish Agricultural Advisory Centre (DAAC), as Statistics Denmark does not include farms less than 5 hectares. Statistics Denmark is the source for the database kept by FAO (Food and Agriculture Organization of the United Nations). This explains why the number of sheep, goats and horses in FAO and the Danish emission inventory disagree. The larg-est difference is found for horses. In the agricultural census, for 2005 the number of horses is estimated to be 47300. Including horses on small farms and riding schools, however, the number of horses rises to
Aggregated livestock cate-gories as given in IPCC
Subcategories in DIEMA Number of stable type
���� ���
Dairy Cattle 9 Non-Dairy Cattle Calves < ½ yr (bull) 2 Calves < ½ yr (heifer) 2 Bull > ½ yr to slaughter 8 Heifer > ½ yr to calving 9 Cattle for suckling 3 ������ Sows 7 Piglets 5 Slaughter pigs 5
223
approximately 156,000. Based on the ERT recommendations, im-provements to the documentation of number of horses, sheep and goats on small farms, in cooperation with DAAC, is planned.
Stable type: At present, there exist no official statistics concerning the distribution of animals according to stable type. The distribution is, therefore, based on an expert judgement from the Danish Agricultural Advisory Centre (DAAC). Approximately 90-95% of Danish farmers are members of DAAC and DAAC regularly collects statistical data from the farmers on different issues, as well as making recommenda-tions with regard to farm buildings. Hence, DAAC have a very good feeling of which stable types are currently in use. From 2006, all farm-ers have to report which stable type they are using to the Danish Plant Directorate. These information are now included in the inventory and are in overall consonant with the expert judgement from DAAC. An-nex 3D Table 6. shows the stable type for each livestock category 1990 – 2005.
Feed consumption and excretion: The Faculty of Agricultural Sciences (FAS) delivers Danish standards related to feed consumption, manure type in different stable types, nitrogen content in manure, etc. The Danish Normative System for animal excretions is based on data from the Danish Agricultural Advisory Centre (DAAC). DAAC is the cen-tral office for all Danish agricultural advisory services. DAAC carries out a considerable amount of research itself, as well as collecting effi-cacy reports from the Danish farmers for dairy production, meat pro-duction, pig production, etc., to optimise productivity in Danish agri-culture. In total, feed plans from 15-18% of the Danish dairy produc-tion, 25-30% of the pig production, 80-90% of the poultry production and approximately 100% of the fur production are collected. These ba-sic feeding plans are used to develop the Danish Normative System. For dairy cows, approximately 800 feeding plans are used to develop the norm figures. Previously, the standards were updated and pub-lished every third or fourth year – the last one is Poulsen et al. from 2001. These standards have been described and published in English in Poulsen & Kristensen (1998).From 2001, NERI receives updated data annually directly from FAS and the data is available at the homepage of FAS (http://www.ag-rsci.dk/content/advancedsearch?SearchTex-t=normtal
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Most of the agricultural emission sources can be considered as key sources for both the emission level and trend (Table 6.4). The most im-portant key source is the N2O emission from agricultural soils, which contributes with 8% of the total national GHG emission in 2005.
�� ����� Key source identification from the agricultural sector 2004
The major part of the agricultural CH4 emission originates from diges-tive processes. In 2005, this source accounts for 27% of the total GHG emission from agricultural activities. The emission is primarily related to ruminants and, in Denmark, particularly to cattle, which, in 2005, contributed with 85% of the emission from enteric fermentation. The emission from pig production is the second largest source and covers 11% of the total emission from enteric fermentation (Figure 6.3), fol-lowed by horses (3%) and sheep and goats (1%).
����� ����� ������������
/�$�������������������The implied emission factors for all animal categories are based on the Tier 2 approach. Feed consumption for all animal categories is based on the Danish normative figures (Poulsen et al. 2001). The normative data are based on actual efficacy feeding controls or actual feeding plans at farm level, collected by DAAC or FAS. For cattle, approxi-mately 20% of the herd is included and for pigs, approximately 35% are included. The data is given in Danish feed units or kg feedstuff and is converted to mega joule (MJ). For grassing animals the energy content in the winter periods feed plan and the energy plan in grass are distinguished between. In Annex 3D Table 1a, the annual average feed intake is shown, from 1990 to 2005, for each CRF livestock cate-gory used in the Danish emission inventory and Table 1b for cattle and swine subcategories. Annex 3D, Table 2 provides additional informa-tion about grassing days for each livestock category. Default values for the methane conversion rate (Ym) given by the IPCC are used for all livestock categories, except for dairy cattle, heifers and suckling cattle, where a national Ym is used for all years (Annex 3D Table 3). New in-vestigations from FAS have shown a change in fodder practice from use of sugarbeets to use of maize, which is now more common. This development in fodder practice reflects the change in the average Ym from 6.39 in 1990 to 5.94 in 2005.
Table 6.5 shows the implied emission factors for all IPCC livestock categories. Due to changed data for feed consumption, in allocation of subcategories and grass conditions, the implied emission factor may vary across the years. Cattle and pigs are the most important emission sources. The category “Non-Dairy Cattle” includes calves, heifers, bulls and suckler cows and the implied emission factor is a weighted average of these different subcategories. The category “Swine” in-cludes the subcategories sows, piglets and slaughter pigs.
No default values are recommended in the IPCC Reference Manual or Good Practice Guidance for poultry and fur farming. The enteric emis-sion from poultry and fur farming is considered non-significant.
The increase in the implied emission factor (IEF) for dairy cattle from 1990-2005 is the result of increasing feed consumption due to rising milk yields. On average, the milk yield has increased from 6200 litre per cow per year in 1990 to approximately 8300 litre per cow per year in 2005 (Statistics Denmark). Based on the ERT recommendations, an interpolation on feed intake from 1990 to 1994 for dairy cattle have been performed to avoid significant inter annual change in values from 1993 to 1994.
The development 1990 - 2003 in IEF for “Non-Dairy Cattle” shows an increase (Table 6.6). This is due to changes in allocation of the subcate-gories. The share of calves, which have the lowest emission factor, has decreased from 1990 to 2005. An increasing part of the bull calves are slaughtered or exported for slaughter or fattening. The lower IEF for 2004 and 2005 are presumably due to a fall in number of heifer and suckling cattle.
The Danish IEF for non-dairy cattle is lower compared with the de-fault value given in the IPCC Reference Manual. This is mainly due to lower weight, lower feed intake and a lower Ym-value compared with the default values provided by the IPCC.
10. Other (fur farming) NE NE NE NE NE NE NE NE NE NE
� ��� ���� �� ��� ���� ���
1. Cattle
a. Dairy 117.21 119.31 121.46 124.12 126.66 128.41
b. Non-Dairy 35.55 35.73 35.85 35.72 35.11 34.90
3. Sheep 17.17 17.17 17.17 17.17 17.17 17.17
4. Goats 13.15 13.15 13.15 13.15 13.15 13.15
6. Horses 21.34 21.34 21.34 21.34 21.34 21.34
8. Swine 1.11 1.09 1.09 1.09 1.11 1.05
9. Poultry NE NE NE NE NE
10. Other (fur farming) NE NE NE NE NE
Non Dairy Cattle – subcategories
(Based on an one year production)
Energy intake
[MJ/day]
Methane conversion
rate (Ym) [%]
IEF – kg [CH4/head/yr]
Calves, bull (0-6 month) 30.8 4.00 8.07
Calves, heifer (0-6 month) 42.8 5.94 16.02
Bull (6 month to slaughter) large breed: 440 kg sl. weight
jersey: 330 kg sl. weight
63.5 4.00 16.61
Heifer (6 month to calving) 105.8 5.94 41.23
Suckling cattle 160.9 5.94 62.48
�������������� ���!����� �� � � � � ������
226
The yearly changes for pigs primarily reflect the changes in the alloca-tion of the subcategories. The feed intake for sows and piglets has in-creased while the feed intake for slaughtering pigs has decreased as a result of improved fodder efficacy (Annex 3D table 1b).
The same feed intake for sheep, goats and horses are used for all years, which results in an unaltered IEF. The IEF for sheep and goats in-cludes lambs and kids, which corresponds to the Danish normative data. This explains why the Danish IEF is nearly twice as high as the IPCC default value.
��������������In Table 6.7, the development in the number of animals from the agri-cultural statistics (Statistics Denmark) and DAAC from 1990 to 2005 is presented. The agricultural census does not include farms less than 5 ha. In the Danish emission inventory, the decision has been made to add number of sheep, goats and horses on small farms based on in-formation from DAAC (see Chapter 1.1.1 – number of animals).
Since 1990, the number of swine and poultry has increased, in contrast to the number of cattle, which has decreased. Buffalo, camels and lla-mas, mules and donkeys are not relevant for Denmark.
�� ����� Number of animals from 1990 to 2005 [1000 head]
* Including animals on small farms (less than 5 ha), which are not covered by Statistics Denmark.
����� %����������������������
The total emission from enteric fermentation is given in Table 6.8. From 1990 to 2005, the emission has decreased by 19%, which is pri-marily related to a decrease in the number of dairy cattle from 753,000 in 1990 to 558,000 in 2005. The number of pigs has increased from 9.5
The emissions of CH4 and N2O from manure management are given in CRF Table 4.B (a) and 4.B (b). This source contributes with 16% of the total emission from the agricultural sector in 2005 and the major part of the emission originates from the production of swine (55%) fol-lowed by cattle production (34%). The remaining part is mainly from poultry (8%).
����� ����� ������������
�7����������The IPCC Tier 2 approaches are used for the estimation of the CH4 emission from manure management. The amount of manure is calcu-lated for each combination of livestock subcategory and stable type and then aggregated to the IPCC livestock categories.
The estimation is based on national data for feed consumption (Poulsen et al. 2001) and standards for digestibility. These data are given in Annex 3D, Tables 4 and 5. For ash content the IPCC standards are used – i.e. 8 percent for ruminants and 2 percent for other live-stock. Default values provided in the IPCC guidelines for the methane production Bo and MCF are used. For liquid systems, the MCF of 10%
Total (Gg CH4) 136.28 138.89 135.20 133.21 128.81 125.24
Total (Gg CO2 eqv.) 2862 2917 2839 2797 2705 2630
228
in the Reference Manual (IPCC, 1997) is used, which is based on Husted (1996). In the Good Practice Guidance (IPCC, 2000), the MCF for liquid manure has been changed from 10% to 39% for cold cli-mates. The results from both Husted (1996) and Massé et al. (2003) in-dicate that the MCF of 10% reflects the Danish conditions better than MCF of 39%. Husted (1996) is, among other sources, based on meas-urements in Danish stables. Investigations described in Massé et al. (2003) are based on measurements in Canadian agricultural conditions similar to the Danish conditions.
Biogas plants using animal slurry reduce the emission of CH4 and N2O (Sommer et al. 2001). This reduction is included in the emission inven-tory. The reduced emission from biogas-treated slurry is included in the emission from dairy cattle and pigs for slaughter, which is the main source of the production of slurry.
In 2005, approximately 7% (0.95 M tonnes of cattle slurry and 1.16 M tonnes of pig slurry) were treated in biogas plants (DEA 2005). The re-duction in the CH4 emission is based on model calculations for an av-erage size biogas plant with a capacity of 550 m3 per day. For methane, a reduction potential of 30% for cattle slurry and 50% for pig slurry is obtained (Nielsen et al. 2002, Sommer et al.�2001). Due to the biogas plants, the total emission of CH4 is reduced by 0.93 Gg CH4 (Table 6.9), which correspond a 2% reduction of the CH4 emission from manure management in 2005.
where CH4 reduction is the reduction in the amount of methane from livestock type �, VS treated slurry is the amount of treated slurry, B0 is the maximum methane forming capacity, MCF is the methane conver-sion factor and RCH4-potential is the reduction potential.
�7�-��$�������������������Table 6.10 shows the development in the implied emission factors from 1990 to 2005. Variations between the years reflect changes in feed intake, allocation of subcategories and changes in stable type system.
IEF for dairy cattle has increased as a result of an increasing milk yield, but also because of change in stable types. In Annex 3D, Table 6 shows the changes in stable types from 1990 to 2005. Old-style tether-
229
ing systems with solid manure have been replaced by loose-housing with slurry-based systems. The MCF for liquid manure is ten times higher than that for solid manure. For pigs, there has been a similar development with a move from solid manure to slurry-based systems. Updated stable type data for 2005 (see Chapter 1.1.1 – stable system) shows fewer animals on slurry systems than previous estimated by the expert judgement from the Danish Agricultural Advisory Centre. This can explain the lower IEF in 2005 for dairy cattle and swine.
For non-dairy cattle, the opposite development has taken place. An in-creasing proportion of bull-calves are raised in stables with deep litter, where the MCF is lower than for liquid manure.
The IEF for sheep and goats includes lambs and kids, which corres-pond the Danish normative data. This explains why the Danish IEF is nearly twice as high as the IPCC default value.
The new CRF format allows registering of emissions from “Other” - see CRF Table 4s1. Denmark produces 2.5 million mink and fox and these contribute with 3 percent of the CH4 emission from manure management. The IEF for fur farming is rising from 0.20 in 1990 to 0.54 in 2005 due to a development against more mink on slurry based sys-tems.
4�+���������The N2O emission from manure management is based on the amount of nitrogen in the manure in stables. The emission from manure de-posits on grass is included in “Animal Production” (Section 6.4.2.2). The IPCC default emission values are applied, i.e. 2.0% of the N-excretion for solid manure, 0.1% for liquid manure and 0.5% from poultry in stable systems without bedding. Nitrogen from poultry, without bedding, contributes less than 1% to the total amount of nitro-gen in manure.
10. Other (Fur farming) 0.35 0.40 0.44 0.47 0.51 0.54
230
The total amount of nitrogen in manure has decreased by 8% from 1990 to 2005 (Table 6.11) and the N2O emission has followed this de-velopment, despite the increasing production of pigs and poultry. This reduction is particularly due to an improvement in fodder efficiency, especially for slaughter pigs. Another reason is the lower emission fac-tor for liquid manure than for solid manure. The development from the previous more traditional tethering systems with solid manure to slurry based system leads to a reduction in the emission of N2O.
It is important to point out that the N-excretion rates shown in Table 6.10 are values weighted for the subcategories (Table 6.3). N-excretion reflects nitrogen excreted per animal per year. The variations in N-excretion in the time-series reflect changes in feed intake, fodder effi-ciency and allocation of subcategories.
The effects from biogas-treated slurry are included in the N2O-emission. Investigation shows that it is possible to reduce the N2O emission from biogas-treated slurry. No description in IPCC Reference Manual or GPG refers how to provide this reduction, why this estima-tion is based on Danish studies (Nielsen et al. 2002, Sommer et al.�2001). Results from these investigations indicate that the reduction po-tential of N2O emission from biogas treated cattle slurry are approxi-mately 36% and 40% from pig slurry. The average nitrogen content in slurry is 0.00538% for cattle slurry and 0.00541% for pig slurry. The re-duced emission is included in the N2O emission from manure man-agement.
In Table 6.13, the total emission from manure management from 1990 to 2005 is shown. The N2O emission has decreased by 20%. The total emission from manure management has, nevertheless, increased by 10% in CO2 equivalents due to the increase in the CH4 emission.
�� ������ Emissions of N2O and CH4 from Manure Management 1990 – 2005
Figure 6.6 shows the distribution and the development from 1990 to 2005 according to different sources. The main part of the emission originates as direct emission. The largest sources here are manure and fertiliser applied on agricultural soils. Another large source is the indi-rect N2O emission, of which the emission from nitrogen leaching is an essential part. The category “Other” includes the emission from sew-age sludge and sludge from industry used as fertiliser.
0
5
10
15
20
25
30
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
�����
�
Other
Indirect Emissions
Animal Production
Direct Soil Emissions
���������� N2O emissions from agricultural soils 1990 - 2005.
����� ����� ������������
Emissions of N2O are closely related to the nitrogen balance. The IPCC Tier 1a methodology is used to calculate the N2O emission. The N2O emission factors for all sources are based on the default values given in IPCC (2000), except for cultivation of histosols, which is based on a na-tional factor. National data for the evaporation of ammonia is applied from the ammonia emission inventory, which is described in more de-tail in Mikkelsen et al. 2006 and Denmark’s annual inventory report, due to the UNECE-Convention on Longe-Range Transboundary Air Pollution (Illerup et al.�2005). These reports are available on the inter-net. A N2O emission survey is presented in Table 6.14. The estimated emissions from the different sub-sources are described in brief in the text which follows.
233
�� ������ Emissions factor - N2O emission from the Agricultural Soils 1990 - 2005
Agricultural soils – emission sources
CRF table 4.D
Ammonia emission
(national data)
N2O emission
(national value)
N2O emission
(IPCC default value)
kg N2O -N/kg N
1. Direct Soil Emissions
Synthetic Fertiliser Applied to Soils NH3 emission = 2% 0.0125
Animal Wastes Applied to Soils NH3 emission = (31-25%) 0.0125
N-fixing Crops 0.0125
Crop Residue 0.0125
Cultivation of Histosols 3 kg N2O-N/ha
2. Animal Production NH3 emission = 7% 0.02
3. Indirect Soil Emissions
Atmospheric Deposition 0.01
Nitrogen Leaching and Runoff 0.025
4. Other
Industrial Waste Used as Fertiliser 0.0125
Sewage Sludge Used as Fertiliser 0.0125
.������0������������� ���� ���� ��The amount of nitrogen (N) applied to soil via use of synthetic fertil-iser is estimated from sales estimates from the Danish Plant Director-ate, the source for the FAO database. Table 6.15 shows the consump-tion of each fertiliser type. Furthermore, the ammonia emission factor for each fertiliser is given, based on national estimates from FAS (Sommer and Christensen 1992, Sommer and Jensen 1994, Sommer and Ersbøll 1996). These emission factors are also in accordance with the emission factors recommended in the inventory guidebook for CLRTAP Emission Inventories – Table 5.1. The Danish value for the FracGASF is estimated at 0.02 and is considerably lower than that from the IPCC, i.e. 0.10. The ammonia emission depends on fertiliser type and the major part of the Danish emission is related to the use of cal-cium ammonium nitrate and NPK fertiliser, where the emission factor is 0.02 kg NH3-N/kg N. The low Danish FracGASF is also probably due to the small consumption of urea (<1%), which has a high emis-sion factor.
234
�� ������ Synthetic fertiliser consumption 2005 and the NH3 emission factors.
1 Danish Institute of Agricultural Sciences (Sommer and Christensen 1992, Sommer and Jen-sen1994, Sommer and Ersbøll 1996) 2 The Danish Plant Directorate
The use of mineral fertiliser includes fertiliser used in parks, golf courses and private gardens. 1% of the mineral fertiliser can be related to these uses outside the agricultural area.
As a result of increasing requirements for improved use of nitrogen in livestock manure and reduce the nitrogen loss to the environment, the consumption of nitrogen in synthetic fertiliser has halved from 1990 to 2005 (Table 6.16).
�� ������ Nitrogen applied as manure to agricultural soils 1990 - 2005
����� ����� ���������The amount of nitrogen applied to soil is estimated as the N-excretion in stables minus the ammonia emission, which occur in stables, under storage and in relation to the application of manure. These values are based on national estimations and are calculated in the ammonia emission inventory (Table 6.17). The total N-excretion in stables from 1990 to 2005 has decreased by 7%. Despite this reduction in N-excretion, the amount of nitrogen applied to soil remains almost unal-tered, due to the reduction in the ammonia emission.
�� ������ Nitrogen applied as manure to agricultural soils 1990 - 2005
The FracGASM express the fraction of total N-excretion (N ab animal) that is volatilised as ammonia emission in stables, storage and applica-tion. The FracGASM has decreased from 0.26 in 1990 to 0.21 in 2005 (Table 6.18). This is the result of an active strategy to improve the utili-sation of the nitrogen in manure.
�� ������ FracGASM 1990 - 2005
�������������To estimate the emission from N-fixing crops, IPCC Tier 1b is applied. The emission calculated is based on nitrogen content, the fraction of dry matter and the content of protein for each harvest crop type. Data for crop yield is based on data from Statistics Denmark. For nitrogen content in the plants, the data is taken from Danish feedstuff tables (Danish Agricultural Advisory Centre). The estimates for the amount of nitrogen fixed in crops are made by the Danish Institute of Agricul-tural Science (Kristensen 2003, Høgh-Jensen et al. 1998, Kyllingsbæk 2000).
�����������������= nitrogen percentage in root and stubble
������= percentage of nitrogen which is fixed
EF��� = the IPCC standard value of 1.25 percent
The Danish inventory includes emissions from clover-grass, despite the fact that this source is not mentioned in the IPCC GPG. Area with grass and clover covered approximately 16% of the total agricultural area in 2005 and, for this reason, represents an important contributor to the total emission from N-fixing crops.
In Table 6.19 the background data for estimating the N-fixing is listed. The emission from N-fixing crops decreases from 1990-2005, largely due to a reduction in agricultural area.
�� ������ Emissions from N-fixing crops 2005
* Dry matter content for straw is 0.87 and the N-fraction is 0.010.
** Average - assumed that N-fix for red clover is 200 kg N/ha and 180 kg N/ha for white clover (Kyllingsbæk 2000)
������ ��� �N2O emissions from crop residues are calculated as the total above-ground quantity of crop residue returned to soil. For cereals, the aboveground residues are calculated as the amount of straw plus stubble and husks. The total amount of straw is given in the annual census and reduced by the amount used for feeding, bedding and bio-fuel in power plants. Straw for feed and bedding is subtracted because this quantity of removed nitrogen returns to the soil via manure.
��
�
������������ �
�����������������
��������
�� ������
������
����������� 21
,,.,
,,2 *))((*
, ∑ +++=−
where� � is the crop, � is the year, �� is the area on which the crop is grown, N� is nitrogen derived from husks, stubble, plant tops and leaf debris in kg ha-1, N � �������� �������� is the number of years between ploughing and EFN2O is the IPCC standard emission factor 1.25%.
N2O emission
from nitrogen fixing crops
Dry matter Fraction [%]
N-Fraction
[% of DM]
N-fixing variations 1990-2005
[kg N/ha]
N-fixing
2005
[kg N/ha]
N-fixing total 2005 [kg N fix]
Pulses* 0.85 0.0337 96-179 131 1 975
Lucerne 0.21 0.0064 307-517 444 1 878
Cereals and pulses for green fodder 0.23 0.0061 16-38 23 1 679
Pulses, fodder cabbage etc. 0.23 0.0061 0-1 NO NO
Peas for canning* 0.85 0.0337 76-139 105 309
Seeds for sowing NE NE 181-186* 181 954
Grass and clover field in rotation 0.13 0.0052 41-104 104 24 847
National values for nitrogen content are used provided by the Faculty of Agricultural Sciences (Djurhuus and Hansen 2003). It is calculated based on relatively few observations, but is at present the best avail-able data. Data for yield and area cultivated are collected from Statistic Denmark. Background data is given in Annex 3D, Table 7.
The total emission from crop residues has decreased 10% from 1990 to 2005 (Table 6.20). This decrease and the fall in FracR is a result of a de-crease in the cultivated area of beets for feeding, which has been re-placed by cultivation of green maize. Another reason is a fall in the ag-ricultural area and a greater part of the straw is harvest – 48% in 1990 and 59% in 2005.
�� ����� Emissions from crop residue 1990 – 2005.�
����������������������N2O emissions from histosols are based on the area with organic soils multiplied by the emission factor for C, the C:N relationship for the organic matter in the histosols and an emission factor of 1.25 of the to-tal amount of released N. See the LULUCF section for further descrip-tion.
�� ����� Activity data – cultivation of histosols (ha)
�������#��������The amount of nitrogen deposited on grass is based on estimations from the ammonia inventory. It is assumed that 15%, on average, of the nitrogen from dairy cattle is excreted on grass (expert judgement from the Danish Institute of Agricultural Science – Poulsen et al. 2001). N-excretion on grass has decreased due to a reduction in the number of dairy cattle. An ammonia emission factor of 7% is used for all ani-
mal categories based on investigations from the Netherlands and the United Kingdom (Jarvis et al. 1989a, Jarvis et al., 1989b and Bussink 1994).
�� ���� Nitrogen excreted on grass 1990 - 2005
FracGRAZ is estimated as the volatile fraction from grazing animals compared with the total excreted nitrogen (N ab animal) (Table 6.23). The decrease in FracGRAZ is due to fall in the production of grassing animals e.g. cattle. A still increasing part of the total N-excretion is re-lated to the production of swine – 45% in 2005 compared with 38% in 1990.
�� ����� FracGRAZ 1990 - 2005
/��������0��������������� ���� �������Atmospheric deposition includes all ammonia emissions sources in-cluded in the Danish ammonia emission inventory (Illerup et al. 2005). This includes the emission from livestock manure, use of synthetic fer-tiliser, crops, ammonia-treated straw used as feed and sewage sludge plus sludge from industrial production applied to agricultural soils.
The emission from atmospheric deposition has decreased from 1990 – 2005 as a result of the reduction in the total ammonia emission, from 109400 tonnes of NH3-N in 1990 to 73600 in 2005.
�� ����� Ammonia emission 2005 (DIEMA)
Ammonia emission 2005
Tonnes NH3-N
Manure 57 500
Synthetic fertiliser 4 600
Crops 11 300
NH3 treated straw 400
Sewage sludge and sludge from the industrial production
100
Emission total 73 600
N2O emission (Gg) 1.16
����� ��� ������������������The amount of nitrogen lost by leaching and run-off from 1986 to 2002 has been calculated by FAS. The calculation is based on two different model predictions, SKEP/Daisy and N-Les2 (Børgesen and Grant, 2003), and for both models measurements from field studies are taken into account. The results of the two models differ only marginally. The
average of the two model predictions is used in the emission inven-tory.
Figure 6.8 shows leaching estimated in relation to the nitrogen applied to agricultural soils as livestock manure, synthetic fertiliser and sludge. The average proportion of nitrogen leaching and runoff has decreased from 39% in the middle of the nineties to 34% in 2002. 33.5% is used in the calculations for 2002-2005. The decline is due to an im-provement in the utilisation of nitrogen in manure. The reduction in nitrogen applied is particularly due to the fall in the use of synthetic fertiliser, which has reduced by more than 50% from 1990 to 2005.
The proportion of N input to soils lost through leaching and runoff (FracLEACH) used in the Danish emission inventory is higher than the default value of the IPCC (30%). FracLEACH has decreased from 1990 and onwards. At the beginning of 1990s, manure was often applied in autumn. The high values are partly due to the humid Danish climate, with the precipitation surplus during winter causing a downward movement of dissolved nitrogen. The decrease in FracLEACH over time is caused by sharpened environmental requirements, banning manure application after harvest. The major part of manure application is made in spring and summer, where there is a precipitation deficit. The overall effect is that FracLEACH has decreased. The data reflects the Danish conditions and is considered as a best estimate.
0
100000
200000
300000
400000
500000
600000
700000
800000
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
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2001
2002
2003
2004
2005
����������������������
30
32
34
36
38
40
42
44
46
48
50
���������������� ���������������
Nitrogen applied on soil
N-leaching and run-off
Fraction of N-leacing and run-
���������� Nitrogen applied to agricultural soils and N-leaching from 1990 to 2005
+����0��������The category, “Other”, includes sewage sludge and sludge from the industrial production applied to agricultural soils as fertiliser. Infor-mation about industrial waste, sewage sludge applied on agricultural soil and the content of nitrogen is provided by the Danish Environ-mental Protection Agency. It is assumed that 1.9% of N-input applied to soil volatises as ammonia.
240
�� ����� Nitrogen in sludge applied on agricultural soils 1990 - 2005
����� ��������������
Table 6.26 provides an overview on activity data from 1990 to 2005 used in relation to the estimation of N2O emission from agricultural soils. The amount of nitrogen applied to agricultural soil has de-creased from 1090 Gg N to 749 Gg N, corresponding to a 31% reduc-tion, which results in a lower N2O emission.
�� ����� Activity data – estimation of N2O emission from agricultural soils 1990 – 2005 [Gg N]
����� %����������������������
The N2O emissions from agricultural soils have reduced by 31% from 1990 to 2005. This is largely due to a decrease in the use of synthetic fertiliser and a decrease in N-leaching as a result of national environ-mental policy, where action plans have focused on decreasing the ni-trogen losses and on improving the nitrogen utilisation in manure.
Total amount of nitrogen applied on soil 818 798 765 749 755 749
1. Direct Emissions
Synthetic Fertiliser 246 229 206 197 202 202
Animal Waste Applied 174 177 181 181 183 181
N-fixing Crops 39 36 34 32 31 35
Crop Residue 56 57 53 53 53 53
2. Animal Production 31 31 30 29 29 28
3. Indirect Emissions
Atmospheric Deposition 84 83 81 77 78 74
N-leaching and Runoff 179 174 168 169 166 164
4. Other
Industrial Waste 5 7 8 8 10 10
Sewage Sludge 4 3 4 4 3 3
242
�� ����� Emissions of N2O from Agricultural Soils 1990 – 2005 [Gg N2O]
��� 4�9+����������
Less than 1% of the NMVOC emission originates from the agricultural sector, which, in the Danish emission inventory, includes emission from arable land crops and grassland. Activity data is obtained from Statistics Denmark. The emission factor for land with arable crops is 393 g NMVOC/ha and for grassland, 2120 g NMVOC/ha (Fenhann and Kilde 1994), (Priemé and Christensen 1991).
�� ����� NMVOC emission from agricultural soils 1990 - 2005
Table 6.29 shows the estimated uncertainties for some of the emission sources, based on expert judgement (Olesen et al. 2001, Gyldenkærne, pers. comm., 2005). The uncertainties for the number of animals and the number of hectares with different crops under cultivation are very small.
Due to the large number of farms included in the norm figures, the arithmetic mean can be assumed as a very good estimate, with a low uncertainty. Cattle and pigs are the most important animal categories for Denmark. All cattle have their own ID-number (ear tags) and, hence, the uncertainty in this number is almost non-existent. Statistics Denmark has estimated the uncertainty in the number of pigs to be less than 1%. The combined effect of low uncertainty in actual animal numbers, feed consumption and excretion rates gives a very low un-certainty in the activity data. The major uncertainty, therefore, relates to the emission factors.
The normative figures (Poulsen et al. 2001) are arithmetic means. Based on the feeding plans, the standard deviation in N-excretion rates between farms can be estimated to ±20% for all animal types (Hanne D. Poulsen, FAS, pers. comm).
In general, the Tier 1 uncertainty is used in the emission factors. A normal distribution is assumed. In the future it will be considered to investigate the possibilities to use Tier 2 uncertainties calculation to improve the outcome from the uncertainty analysis.
The highest uncertainty is connected with manure management. The emission factor for CH4 from manure management is 10%. This figure may be underestimated and the uncertainty is, therefore, increased to 100% until further investigations reveal new data.
244
�� ����� Estimated uncertainties associated with activities and emission factors for CH4 and N2O
�"� ����������������������:���������������������
A general QA/QC plan for the agricultural sector is under develop-ment. The following Points of Measures (PM) are taken into account in the inventory for 2005.
Data Storage
level 1
1. Accuracy DS.1.1.1 General level of uncertainty for every data-set including the reasoning for the specific values.
The following external data are in used in the agricultural sector:
• Data from the annual agricultural census made by Statistics Den-mark
• The Faculty of Agricultural Sciences, University of Aarhus (FAS) • The Danish Plant Directorate • Danish Agricultural Advisory Centre (DAAC) • The Danish Energy Authority • Danish Environmental Protection Agency The emission factors come from various sources:
• IPCC guidelines • The Faculty of Agricultural Sciences, University of Aarhus (FAS):
NH3 emission, CH4 emission from enteric fermentation and manure management.
Statistics Denmark The agricultural census made by Statistics Denmark is the main sup-ply of basic agricultural data. In Denmark, all cattle, sheep and goats have to be registered individually and hence the uncertainty in the data is negligible. For all other animal types, farms having more than 10 animal units are registered.
The Faculty of Agricultural Sciences (FAS) FAS are responsible for the delivery of N-excretion data for all animal and housing types. Data on feeding consumption on commercial farms are collected annually by DAAC from on-farm efficacy controls. For dairy cattle, data is collected from 15-20% of all farms, for pigs, 25-30% and for poultry and mink, 90-100% of all farms. The farm data are used to calculate average N-excretion from different animal and hous-ing types. Due to the large amount of farm data involved in the data-set, N-excretion is seen as a very good estimate for average N-excretion at the Danish livestock production
Danish Plant Directorate Total area with the various agricultural crops is provided to the Dan-ish Plant Directorate via the agricultural subsidy system. For every parcel of land (via a vector-based field map with a resolution of >0.01 ha), the area planted with different crops is reported. If the total crop area within a parcel is larger than the parcel area, a manual control of the information is performed by the Plant Directorate. The area with different crops, therefore, represents a very precise estimate.
All farmers are obliged to do N-mineral accounting on a farm and field level with the N-excretion data from FAS. Data at farm level is reported annually to the Danish Plant Directorate. The N figures also include the quantities of mineral fertilisers bought and sold. Suppliers of mineral fertilisers are required to report all N sales to commercial farmers to the Plant Directorate. The total sold to farmers is very close to the amount imported by the suppliers, corrected for storage. The to-tal amount of mineral fertiliser in Denmark is, therefore, a very precise estimate for the mineral fertiliser consumed. This is also valid for N-excretion in animal manure.
The Danish Plant Directorate, as the controlling authority, performs analysis of feed sold to farmers. On average, 1600 to 2000 samples are analysed every year. Uncertainty in the data is seen as negligible. The data are used when estimating average energy in feedstuffs for pigs, poultry, fur animals, etc.
From 2005 the Danish Plant Directorate provides data for distribution of stable type.
Danish Agricultural Advisory Centre (DAAC) DAAC is the central office for all Danish agricultural advisory ser-vices. DAAC carries out a considerable amount of research itself, as well as collecting efficacy reports from the Danish farmers for dairy production, meat production, pig production, etc., to optimise produc-tivity in Danish agriculture. From DAAC data on stable type until 2004, grassing situation and information on application of manure is received.
246
The Danish Energy Authority The amount of slurry treated in biogas plants is received from the Danish Energy Authority.
Danish Environmental Protection Agency Information on the sludge from waste water treatment and the manu-facturing industry and the amount applied on agricultural soil is ob-tained from the Danish Environmental Protection Agency.
Uncertainty in the data received is very low due to the very strict envi-ronmental laws in Denmark. Standard deviation regarding the num-bers of cattle and pigs has been estimated to <0.7%. For poultry the standard deviation is <2.1%. For all years, 25-35% of all holdings are included in the census. The standard deviation for N-excretion be-tween farms is reported as 25% for dairy cattle and pigs, but due to the large numbers involved in the estimation of the average N-excretion, the average is assumed to be a precise estimate for the Danish agricul-tural efficacy level.
The Danish N-excretion levels are generally lower than IPCC default values. This is due to the highly skilled, professional and trained farm-ers in Denmark, with access to a highly competent advisory system.
The feed consumption per animal is in line with similar data from Sweden, although they are not quite comparable because Denmark is using feeding units (FE) which cannot easily be converted to energy content. Earlier, one feeding unit was defined as one kg of barley. To-day, the calculations are more complicated and depend on animal type.
See DS 1.1.1.
External data received are stored in the agricultural directory in NERI’s IT system.
Data Storage
level 1
1. Accuracy DS.1.1.2 Quantification of the uncertainty level of every single data value including the reasoning for the specific values.
Data Storage
level 1
2.Comparability DS.1.2.1 Comparability of the data values with similar data from other countries, which are compa-rable with Denmark, and evaluation of dis-crepancy.
Data Storage
level 1
3.Completeness DS.1.3.1 Documentation showing that all possible national data sources are included by setting down the reasoning behind the selection of datasets.
Data Storage
level 1
4.Consistency DS.1.4.1 The origin of external data has to be pre-served whenever possible without explicit arguments (referring to other PMs).
247
NERI has established formal data agreements with all institutes and organisations which deliver data, to assure that the necessary data is available to prepare the inventory on time.
Please refer to DS 1.1.1.
A great deal of documentation already exists in the literature list. A separate list of references is stored in: I:/rosproj/luft_emi/inventory-/2005/4_Agriculture/level_1a_storage/
6.Robustness DS.1.6.1 Explicit agreements between the external institution holding the data and NERI about the conditions of delivery.
Data Storage
level 1
7.Transparency DS.1.7.1 Summary of each dataset including the rea-soning for selecting the specific dataset.
Data Storage
level 1
7.Transparency DS.1.7.3 References for citation for any external data set have to be available for any single value in any dataset.
Data Storage
level 1
7.Transparency DS.1.7.4 Listing of external contacts for every dataset.
248
The Tier 1 methodology is used to calculate the uncertainties for the agricultural sector. The uncertainties are based on expert judgement (Olesen et al. 2001, Poulsen et al. 2004, Gyldenkærne, pers. comm., 2005) and a normal distribution is assumed. Further work will focus on the possibilities to carry out Monte Carlo simulations to improve the outcome from the uncertainty analysis.
Please refer to DP 1.1.1.
Denmark has recently worked out a report with a more detailed de-scription of the methodological inventory approach (Mikkelsen et al. 2006). This report has been reviewed by the Statistics Sweden, who is responsible for the Swedish agricultural inventory. Furthermore, data sources and calculation methodology developments are discussed in cooperation with specialists and researchers in different institutes and research sections. As a consequence, both the data and methods are evaluated continually according to the latest knowledge and informa-tion.
Enteric CH4 emissions are, in general, lower than the IPCC default values due to the professional way farms are managed in Denmark. Enteric fermentation from dairy cows is high and comparable with North American conditions. Due to the increase in milk production per dairy cow, there has been an increase in enteric fermentation of CH4, and it is in line with the conditions in the USA, the Netherlands and Sweden.
The CH4 emission from manure management is higher than the de-fault IPCC values for Western Europe because of the higher percent-age handled as slurry. However, due to the high efficacy at farm level, energy intake is lower per head and the subsequent CH4 emission from slurry is, thereby, lower. Denmark uses an MCF factor of 10% as provided in the 1996 guidelines and not the 39% in the revision to the 1996 guidelines. For further explanation, see the text in the agriculture chapter (6.3.2).
FracLEACH is higher than the default IPCC values. FracLEACH has de-creased from 1990 and onwards. In the beginning of 1990s, manure was often applied in autumn. The high values are partly due to the humid Danish climate, with the precipitation surplus during winter causing a downward moment of dissolved nitrogen. The decrease in FracLEACH over time is caused by sharpened environmental require-
Data Processing level 1
1. Accuracy DP.1.1.1 Uncertainty assessment for every data source as input to Data Storage level 2 in relation to type of variability. (Distribution as: normal, log normal or other type of variability).
Data Processing level 1
1. Accuracy DP.1.1.2 Uncertainty assessment for every data source as input to Data Storage level 2 in relation to scale of variability (size of variation intervals).
Data Processing level 1
1. Accuracy DP.1.1.3 Evaluation of the methodological approach using international guidelines.
Data Proces-sing level 1
1. Accuracy DP.1.1.4 Verification of calculation results using guide-line values.
249
ments, banning manure application after harvest. As a result, most manure application occurs during spring and summer, where there is a precipitation deficit. The generally accepted leaching values in Den-mark are 0.3 for mineral nitrogen and 0.45 for organic-bound nitrogen. These values are based on numerical leaching studies.
The Danish emission inventory for the agricultural sector mainly meets the request as set down in the IPCC Good Practice Guidance.
All known major sources are included in the inventory.
In Denmark, only very few data are restricted (military installations). Accessibility is not a key issue; it is more lack of data.
The calculation procedure is consistent for all years.
During the development of the model, thorough checks have been made by all persons involved in preparation of the agricultural sec-tion.
Time-series for activity data, emission factors and total emission are performed to check consistency in the methodology, to avoid errors, to identify and explain considerable year to year variations.
During the calculations, the results are checked according to the check-list.
Data Processing
level 1
2.Comparability DP.1.2.1 The inventory calculation has to follow the international guidelines suggested by UNFCCC and IPCC.
Data Processing
level 1
3.Completeness DP.1.3.1 Assessment of the most important quan-titative knowledge which is lacking.
Data Processing
level 1
3.Completeness DP.1.3.2 Assessment of the most important cases where access is lacking with regard to critical data sources that could improve quantitative knowledge.
Data Processing
level 1
4.Consistency DP.1.4.1 In order to keep consistency at a high level, an explicit description of the activi-ties needs to accompany any change in the calculation procedure
Data Processing
level 1
5.Correctness DP.1.5.1 Show at least once, by independent calculation, the correctness of every data manipulation.
Data Processing
level 1
5.Correctness DP.1.5.2 Verification of calculation results using time-series.
Data Processing
level 1
5.Correctness DP.1.5.3 Verification of calculation results using other measures.
250
Output data to Data Storage Level 2 is checked for correctness accord-ing to the check-list.
All calculation principles are described in the NIR and the documenta-tion report (Mikkelsen et al. 2006).
All theoretical reasoning is described in the NIR and the documenta-tion report (Mikkelsen et al. 2006).
All theoretical reasoning is described in the NIR and the documenta-tion report (Mikkelsen et al. 2006).
A clear reference in the DP level 1 to DS level 1 is under construction.
Changes compared with the last emissions report are described in the NIR and the total emission changes is given in a table under the sec-tion, “Recalculation”. The text describes whether the change is caused by changes in the dataset or changes in the methodology used. A log-book is kept in the spreadsheet mentioning all changes.
A manual check-list is under development for correct connection be-tween all data types at level 1 and 2.
A manual check-list is under development for correctness of data im-port to level 2.
Data Processing
level 1
5.Correctness DP.1.5.4 Shows one-to-one correctness between external data sources and the data bases at Data Storage level 2.
Data Processing
level 1
7.Transparency DP.1.7.1 The calculation principle and equations used must be described.
Data Processing
level 1
7.Transparency DP.1.7.2 The theoretical reasoning for all methods must be described.
Data Processing
level 1
7.Transparency DP.1.7.3 Explicit listing of assumptions behind all methods.
Data Processing
level 1
7.Transparency DP.1.7.4 Clear reference to dataset at Data Stor-age level 1.
Data Processing
level 1
7.Transparency DP.1.7.5 A manual log to collect information about recalculations.
Data Storage level 2
5.Correctness DS.2.5.1 Documentation of a correct connection between all data types at level 2 to data at level 1.
Data Storage level 2
5.Correctness DS.2.5.2 Check if a correct data import to level 2 has been made.
251
�;� !������������
Compared with the previous reported emission inventory 1990 - 2004, some changes are made. These changes influence the total GHG emis-sion from the agricultural sector by less than 1% (Table 6.30).
�� ������ Changes in GHG emission in the agricultural sector compared with the CRF reported last year
There have been no changes in the methodology.
The emission from poultry now includes exported living animals – chickens for slaughtering, ducks, geese and turkeys. Data on living exported poultry is available from 1994 and based on information from the Danish Poultry Council.
The Danish normative feeding norms for 2003 are updated. A higher nitrogen excretion for dairy cattle and a lower nitrogen excretion from slaughtering pigs than previous estimated. This has a slight effect on the total GHG emission by 0.03 Gg CO2 equivalents or less than a half percent.
Refer to the ERT recommendation an interpolation on feed intake from 1990 to 1994 has been performed for dairy cattle to avoid jumps in the time-series. The relatively large difference in the IEF for enteric fer-mentation in 1993 and 1994 was a result of unavailable one-year data and reflects a development in milk yield from a four year period (1990 – 1994).
For the first time we have received data from the Danish Plant Direc-torate concerning the contribution of stable type 2005. Previous this has been estimated by expert judgement from the Danish Agricultural Advisory Centre. The new data are in very good accordance with pre-vious estimations and shows only a few differences.
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The Danish emission inventory for the agricultural sector largely meets the request as set down in the IPCC Good Practice Guidance. The Expert Review Team (ERT) has pointed out the needs for more de-
Change in Gg CO2 eqv. 0.00 0.01 0.01 -0.02 -0.02 -0.03 0.04
Change in pct. 0.0 0.1 0.1 -0.2 -0.2 -0.3 0.4
252
tailed documentation, transparency and better explanations, especially related to the use of national data and national methodologies. A documentation report, which includes more information on DIEMA (the model complex system used to calculate both the ammonia and the GHG emission from the agricultural activities), is now available in English (Mikkelsen et al. 2006) and describes in detail the methodol-ogy, the assumptions and use of background data. The final report has been reviewed by the Statistics Sweden, who is responsible for the Swedish agricultural inventory. The response was very positive and they had at the moment no further specific suggestion for improve-ment.
In the years to come, the NIR will focus on improvements in transpar-ency in relation to use of the national values, by means of improved explanations and relevance for tables in the report, and references to more detailed descriptions in other reports or inclusion of summary descriptions in the NIR itself.
In relation to the ERT recommendations, some specific improvements, as mentioned below, are planned:
• the documentation of number of horses, sheep and goats on small farms less than 5 ha, which is not included in the annual census from Statistics Denmark.
• implementation of tables with live-stock information on national data at sub-category level as N-excretion, gross energy intake (GE), Feed digestibility (DE), volatile solids (VS), and methane conver-sion rate (Ym).
The work concerning the QA/QC plan and the estimation of uncer-tainties are continued. The QA/QC plan for the agricultural sector is still under development, but, as a first step, a review of the existing data structure is carried out – see Section 6.7. The further work con-cerning the uncertainties will focus on the possibilities to bring about improvements by using the TIER 2 methodology, which may improve the outcome from the uncertainty analysis.
Other issues which are needed to be improved are the storage of data set. The DIEMA model complex is build up as a number of spread-sheets, where data is linked between the sheets. The systems Achilles heel is the multitude of linked data and every year enlargement. To improve the systems sustainability, it is planned to storage all data set in a data base. At the same time all activity data and emission factors will be updated as the latest knowledge and some changes might be necessary to take into account.
!����������
Bussink, D.W. 1994: Relationship between ammonia volatilisation and nitrogen fertilizer application rate, intake and excretion of herbage ni-trogen by cattle on grazed swards. Fertil. Res. 38, 111-121.
Børgesen, C.D. & Grant, R. 2003: Vandmiljøplan II – modelberegning af kvælstofudvaskning på landsplan, 1984 til 2002. Baggrundsnotat til
253
Vandmiljøplan II - slutevaulering. December 2003, Danmarks Jord-brugsforskning og Danmarks Miljøundersøgelser. (In Danish).
CFR, Common Reporting Format: (http://cdr.eionet.eu.int/dk/Air_ Emission_Inventories/Submission_EU)
Danish Energy Authority, 2004: S. Tafdrup. Pers. Comm., 2004
Djurhuus, J. & Hansen, E.M. 2003: Notat vedr. tørstof og kvælstof i ef-terladte planterester for landbrugsjord – af 21. maj 2003. Forskningscenter Foulum, Tjele. (In Danish).
Fenhann, J. & Kilde, N.A. 1994: Inventory of Emissions to the Air from Danish Sources 1972-1992. System Analysis Department – Risø Na-tional Laboratory.
Husted, 1994: Waste Management, Seasonal Variation in Methane Emission from Stored Slurry and Solid Manures. J. Environ. Qual. 23:585-592 (1994).
Høgh-Jensen, H., Loges, R., Jensen, E.S., Jørgensen, F.V. & Vinther, F.P. 1998: Empirisk model til kvantificering af symbiotisk kvælstoffiksering i bælgplanter. – Kvælstofudvaskning og –balancer i konventionelle og økologiske produktionssystemer (Red. Kristensen E.S. & Olesen, J.E.) s. 69-86, Forskningscenter for Økologisk Jordbrug. (In Danish).
Gyldenkærne, Steen. Researcher at NERI, Departement of Policy Analysis. Pers. Comm., 2005
Illerup, J.B., Lyck, E., Nielsen, M., Winther, M., Mikkelsen, M.H., Hoffmann, L., Gyldenkærne, S., Sørensen, P.B., Fauser, P., Thomsen, M. & Vesterdal, L. (2005): Denmark´s National Inventory Report 2005 - Submitted under the United Nations Framework Convention on Cli-mate Change. 1990-2003. Emission Inventories. National Environ-mental Research Institute. - Research Notes from NERI 211: 416 pp. (electronic). http://www2.dmu.dk/1_viden/2_Publikationer/3_arbrapporter/rapporter/AR211.pdf
IPCC, 1997: Revised 1996 IPCC Guidelines for National Greenhouse Gas Inventories. Available at http://www.ipccnggip.iges.or.jp/public/gl/-invs6.htm (15-04-2007).
IPCC, 2000: Good Practice Guidance and Uncertainty Management in National Greenhouse Gas Inventories. Available at http://www.ipcc-nggip.iges.or.jp/public/gp/english/ (15-04-2007).
Jarvis, S.C., Hatch, D.J. & Roberts, D.H., 1989a: The effects of grassland management on nitrogen losses from grazed swards through ammo-nia volatilization; the relationship to extral N returns from cattle. J. Agric. Sci. Camb. 112,205-216.
Jarvis, S.C., Hatch, D.J. & Lockyer, D.R., 1989b: Ammonia fluxes from grazed grassland annual losses form cattle production systems and their relation to nitrogen inputs. J. Agric. Camp. 113, 99-108.
254
Kristensen, I.S. 2003: Indirekte beregning af N-fiksering - draft, not published. Danmarks JordbrugsForskning. (In Danish).
Kyllingsbæk, 2000: Kvælstofbalancer og kvælstofoverskud i dansk landbrug 1979-1999. DJF rapport nr. 36/markbrug, Dansk Jordbrugs-forskning.
Massé, D.I., Croteau, F., Patni, N.K. & Masse, L. 2003: Methane emis-sions from dairy cow and swine slurries stored at 10ºC and 15ºC. Agri-culture and Agri-Food Canada, Canadian Biosystem Engineering, Vo-lume 45 p. 6.1-6.6
Mikkelsen, M.H., Gyldenkærne, S., Poulsen, H.D., Olesen, J.E. & Sommer, S.G� 2006: Emission of ammonia, nitrous oxide and methane from Danish Agriculture 1985-2002. Methodology and Estimates. Na-tional Environmental Research Institute. - Arbejdsrapport fra DMU 231: 90 pp. (electronic). http://www2.dmu.dk/Pub/AR231.pdf
Nielsen, L.H., Hjort-Gregersen, K., Thygesen, P. & Christensen, J. 2002: Socio-economic analysis of centralised Biogas Plants - with technical and corporate economic analysis, Rapport nr. 136, Fødevareøkono-misk Institut, Copenhagen, pp 130.
Olesen, J.E., Fenhann, J.F., Petersen, S.O., Andersen, J.M. & Jacobsen, B.H. 2001: Emission af drivhusgasser fra dansk landbrug. DJF rapport nr. 47, markbrug, Danmarks Jordbrugsforskning, 2001. (In Danish)
Poulsen, H.D., Børsting, C.F., Rom, H.B. & Sommer, S.G. 2001: Kvæl-stof, fosfor og kalium i husdyrgødning – normtal 2000. DJF rapport nr. 36 – husdyrbrug, Danmarks Jordbrugsforskning. (In Danish)
Poulsen, Hanne Damgaard. The Faculty of Agricultural Science, pers. comm.
Poulsen, H.D. & Kristensen, V.F. 1998: Standards Values for Farm Ma-nure – A revaluation of the Dansih Standards Values concerning the Nitrogen, Phosphorus and Potassium Content of Manure. FAS Report No. 7 - Animal Husbandry. Danish Institute of Agricultural Sciences.
Primé, A. & Christensen, S. 1991: Emission of methane and non-methane volatile organic compounds in Denmark – Sources related to agriculture and natural ecosystems. National Environmental Research Institute. NERI, Technical Report No. 19/1999.
Sommer, S.G., Møller, H.B. & Petersen, S.O. 2001: Reduktion af driv-husgasemission fra gylle og organisk affald ved Biogasbehandling. DJF rapport - Husdyrbrug, 31, 53 pp. (In Danish).
Sommer, S.G. & Christensen, B.T. 1992: Ammonia volatilization after injection of anhydrous ammonia into arable soils of different moisture levels. Plant Soil. 142, 143-146.
Sommer, S.G. & Jensen, C. 1994: Ammonia volatilization from urea and ammoniacal fertilizers surface applied to winter wheat and grass-land. Fert. Res. 37, 85-92.
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Sommer, S.G. & Ersbøll, A.K. 1996: Effect of air flow rate, lime amendments and chemical soil properties on the volatilization of am-monia from fertilizers applied to sandy soils. Biol. Fertil Soils. 21, 53-60.
Statistics Denmark - Agricultural Statistic from year 1990 to 2003. (www.dst.dk)
No changes have been made in the used methodologies since previous year. The methodology for LULUC is described in Gyldenkærne et al. (2005). The LULUCF sector differs form the other sectors in that it con-tains both sources and sinks of carbon dioxide. LULUCF are reported in the new CRF format. Removals are according to the guidelines in the new reporting format given as negative signatures and sinks are reported with positive signatures. This is opposite to the usually way of reporting as given in the remaining report. For 2005 emissions from LULUCF were estimated to be a sink of approximately 1,453 Gg CO2 or 2.3% of the total reported Danish emission.
Approximately 2/3 of the total Danish land area is cultivated. To-gether with high numbers of cattle and pigs there is a high (environ-mental) pressure on the landscape. To reduce the impact an active pol-icy has been adopted to protect the environment. The adopted policy aims at a doubling of the forest area within the next 80-100 years, re-establishing of former wetlands and establishing of protected national parks. In Denmark all natural habitats and forests are protected and therefore, in the inventory, no conversions from forest or wetlands into cropland or grassland are made, because in reality this is not oc-curring.
A thorough GIS analysis of Land Use and Land Use Change has been made for the agricultural sector. The method is described in more de-tail in 7.3 Cropland. A full matrix of the total land area still needs to be carried out. This is expected to take place in 2007 and 2008 by satellite monitoring.
The data are reported in the new CRF format under IPCC categories 5A (Forestry), 5B (Cropland), 5C (Grassland) and 5D (Wetlands). The IPCC categories 5E (Settlements) and 5F (Other) are not reported as these changes are considered to be negligible or not occurring in Den-mark.
Fertilisation of forests and other land is very negligible and therefore reported as a total for all fertiliser consumption under the agricultural sector. Drainage of forest soils is not reported. Liming is included in the LULUCF sector. All liming are reported under Cropland because only very limited amounts are used in forestry and on permanent grassland. Field burning of biomass is prohibited in Denmark and therefore reported as NO. Biomass burned in power plants is reported in the energy sector.
257
In Table 7.1 is given an overview of the emission from the LULUCF sector in Denmark measured in Gg CO2-eqv. Forests are sinks in Den-mark of approximately 3,500 Gg CO2-eqv y-1, however in 2005 this was smaller due to storms. Cropland is estimated to have a net emission of 300-2,400 Gg CO2 y-1. From 1990 and onwards a decrease in the emis-sion from Cropland is estimated due to a higher incorporation of straw (ban of field burning), demands growing of catch crops in the autumn, a reduced agricultural area, an increase in hedgerows and a reduced consumption of lime. The area with reconstructed wetlands has in-creased and consequently the accumulation of organic matter here.
258
�� ����� Overall emission (Gg CO2) from the LULUCF sector in Denmark, 1990-2004
Danish forests cover only a small part of the country (11%) since the dominant land use in Denmark is agriculture. Danish forests are man-aged as closed canopy forests. The main objective is to ensure sustain-able and multiple-use management. The main management system used to be the clear-cut system. Today, principles of nature-based for-est management including continuous cover forestry are being imple-mented in many forest areas, e.g. the state forests (about ¼ of the forest area). Contrary to the situation in the other Scandinavian countries, forestry does not contribute much to the national economy.
The Danish Forest Act protects the main part of the forest area (about 80%) against conversion to other land uses. In principle, the main part of the Danish forests will always remain forest. It is the ambition to enlarge the forested area to 20-25% of the country size by the end of the 21st century. Afforestation of arable land is therefore encouraged by use of subsidies to private landowners. Subsidized afforestation ar-eas are automatically protected as forest reserves. Denmark is the only part of the Kingdom with a forestry sector. Greenland and the Faroe Islands have almost no forest.
Since 1881, a Forestry Census has been carried out roughly every 10 years based on questionnaires to forest owners (Larsen and Johannsen, 2002). The two latest censuses were carried out in 1990 and 2000. Since the data is based on questionnaires and not field observations, the for-est definition may vary slightly but the basic definition of a forest is that the forest area must be minimum 0.5 ha. There is no specific guideline on the crown cover or the height of the trees. Open wood-land and open areas within the forest are not included.
In 1990, the forested area with trees was about 411,000 ha (= 4,110 km2) or approximately 10% of the land area (Forestry Census, 1990). Broad-leaved tree species made up 35% and coniferous species made up 65% of the forest area. See Table 7.2 for the distribution to specific tree spe-cies and species categories.
260
�� ���� Total wooded area, temporarily uncovered area and distribution of forested area to main tree species and species categories in 1990 and 2000. From Statistics Denmark (����� ��������������������).
Area in ha 1990 2000
Total wooded area 417089 473320
Area temporarily without trees1 5702 4985
Broadleaves, total area 143253 174385
Beech 71764 79552
Oak 30247 43011
Ash 10158 12681
Sycamore maple 7979 9444
Other broadleaves 23105 29698
Conifers, total area 268134 293950
Norway spruce 135010 132237
Sitka spruce 35464 34223
Silver fir and other fir 7001 11919
Nordmann’s fir 11841 28173
Noble fir 15115 15498
Other conifers 63703 719011Area not yet replanted with trees following clear-cutting
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���������� Tree species distribution to the total forested area in 2000. From Statistics Denmark (����� ��������������������).
In 2000, the forested area with trees was 468,000 ha or approximately 11% of the land area. The number of respondents for this survey was 32,300, which is considerably higher than the number of 22,300 in the 1990 survey. The number of respondents may cause the changes in the forest area between 1990 and 2000 rather than real changes in the for-est area. The increase in forested area is therefore only partly a result of afforestation of former arable land since 1990 (about 27,536 ha). Broadleaved tree species made up 37% and coniferous species made up 63% of the forest area. See Figure 7.1 and Table 7.2 or the distribu-tion to specific tree species and species categories.
261
Compared with other sectors, forestry has very low energy consump-tion. Green accounting and environmental management are being de-veloped in the sector, partly with the intention to determine whether the use of fossil fuels can be reduced.
Danish forests are managed with special reference to multiple-use and sustainability, and carbon sequestration is just one of several objec-tives. The policy objective most likely to increase carbon sequestration is the 1989 target to double Denmark’s forested area within 100 years. There are several measures aiming at achieving this objective. Firstly, a government subsidy scheme has been established that supports pri-vate afforestation on agricultural land. Secondly, also governmental and municipal afforestation is taking place, and thirdly some private afforestation is taking place without subsidies. The Danish Forest and Nature Agency is responsible for policies on afforestation on private agricultural land and on state-owned land.
"����� ����� ������������
'������������������������������������������������������������Standing stocks of wood in 1990 and 2000, and annual increments for the two periods 1990-99 and 2000-2004 are all obtained from the For-estry Census of 2000 (Larsen and Johannsen, 2002).
The Forestry Census has been carried out roughly every 10 years and is based on questionnaires to forest owners. Detailed information about the census and the methodology can be found in Larsen and Jo-hannsen (2002), and further documentation is available from Danish Centre for Forest, Landscape and Planning7. In short, the estimates of standing volume and volume increments in the Forestry Census from 1990 and from 2000 are based on questionnaire information from for-est owners on forest area distributed to species and age classes, and in-formation on site productivity. Based on standard yield table functions these input data are used to estimate standing volume and rate of in-crement for each tree species category.
In 1990, the standing stock of wood was 64.8 million m3, equivalent to 158 m3 per ha, distributed with 40% broadleaved species and 60% co-niferous species. This stock of wood was equivalent to 22425 Gg C or 82225 Gg CO2. In 2000, the standing stock of wood was 77.9 million m3, equivalent to 166 m3 per ha, distributed with 37% broadleaved species and 63% coniferous species. This stock of wood was equivalent to 26803 Gg C or 98278 Gg CO2. These two figures cannot be compared directly due to the differing numbers of respondents in the two cen-suses. The number of respondents in the 2000 survey was 32300, con-siderably higher than the number of 22300 in the 1990 survey.
From 2002, a new sample-based National Forest Inventory (NFI) has been launched. The new NFI will replace the Forestry Census and measures 1/5 of the plots every year. The NFI will be complete by 2006 (after 5 years of field measurements), and the first background data for use in the NIR is expected during 2007. This type of forest in-
7 Contact: Dr. V.K. Johannsen, Danish Centre for Forest, Landscape and Planning, Hoer-sholm Kongevej 11, DK-2970 Hoersholm, Denmark. E-mail: [email protected]
262
ventory will be quite similar to inventories used in other countries, e.g. Sweden. (see also section 7.2.6).
Expansion factors are needed to convert stem volumes for conifers and total aboveground biomass for the broadleaves to total biomass. There is currently no information on applicable expansion factors for Danish conditions, however, a couple of studies will supply valuable national information within a year or so. Therefore, stemwood volumes for conifers are converted to total biomass by an expansion factor of 1.8 based on Schöne and Schulte (1999), and aboveground biomass for broadleaves are converted to total biomass by an expansion factor of 1.2 based on Vande Walle et al. (2001) and Nihlgård and Lindgren (1977). These studies were chosen as basis for expansion factors due to the geographical closeness of study sites (Germany, Sweden and Bel-gium), and the studies concerned relevant Danish species like beech, oak and Norway spruce. However, stand management may of course be different from Danish “average” stand management, but variability in management may be even larger within Denmark. The difference between expansion factors for conifers and broadleaves is mainly due to the difference in biomass data for the species categories. The total biomass in m3 is converted to dry mass by use of tree species-specific basic wood densities (Moltesen, 1988, see Table 7.3), and carbon con-tent is finally calculated by using a carbon concentration of 0.5 g C g-1 dry mass.
�� ����� Basic wood densities for Danish tree species (Moltesen, 1988).
Wood density
(t dry matter/ m3 fresh volume)
Norway spruce 0.38
Sitka spruce 0.37
Silver fir 0.38
Douglas-fir 0.41
Scots pine 0.43
Mountain pine 0.48
Lodgepole pine 0.37
Larch 0.45
Beech 0.56
Oak 0.57
Ash 0.56
Maple 0.49
The Danish reporting on changes in forest carbon stores only considers the biomass of trees. There is no systematic information available on soil organic carbon for use in the reporting.
��������+����:��������������������$�������3�����*<<>�Net C sequestration in the periods 1990–1999 and 2000-2005 was the result of a net increase in standing stock of the existing forests. Net C sequestration in existing forests is the result of relatively low harvest intensity, especially for conifers. The harvesting intensity for broad-leaves has also been decreasing since the late 90’ies. The high net C se-questration is also partly a result of an uneven age class distribution with relatively many young stands.
263
The estimated gross wood increment for the period 2000–2005 is based on the most recent questionnaire-based Forestry Census of 2000. Har-vesting is not included in estimates of gross wood increment. Mean annual increments (m3 ha-1) for the categories of tree species for the periods 1990-1999 and 2000-2009 are both provided in the Forestry Census of 2000. The gross annual increment for 1990-99 was estimated at 4.6 M m3 y-1 and around 5.2 M m3 y-1 for 2000-09. For the period 1990-99 a new increment estimate was calculated based on information from the 1990 Forestry Census, since missing information on site pro-ductivity now could be replaced by reference values on site productiv-ity from the State Forests. Further details on the calculation of the es-timates can be found in Johannsen (2002).
Data on the annually harvested amount of wood (Figure 7.2) are ob-tained from Statistics Denmark (http://www.statistikbanken.dk/). Commercial harvesting was used in the calculations for broadleaved species as wood from thinning operations in young stands is sold as fuel wood and therefore appears in the statistics. For conifers, non-commercial thinning operations are more common. In order to account for this, 20% were added to the figures for commercial harvests of co-niferous wood.
������� �� Total annual harvest of commercial wood in forests planted before 1990. The peak in 2000 is
caused by windthrow of conifers during the storm on Dec. 3, 1999, and the peak in 2005 is caused by windthrow
of conifers in the storm Jan. 8, 2005. From Statistics Denmark (http://www.statistikbanken.dk/).
The net annual increment (gross wood increment minus harvested wood) was estimated to approximately 2.3 M m3 y-1 for 1990–1999 and is estimated to approximately 2.7 M m3 y-1 for 2000-2005 (Larsen and Johannsen, 2002). Rates of wood increment are converted to CO2 up-take by using the expansion factors, basic wood densities and carbon concentration mentioned above.
264
�� ����� Carbon stock changes in the Danish forests in the years 2003, 2004, and 2005. Calculation examples.
An example of calculations for three individual years is given in Table 7.4. The table shows the different steps of calculation from annual gross increment in order to estimate the net sink for CO2. A summary of gross uptake of CO2 since 1990 is given in Table 7.5. Figure 7.3 shows the dynamics in the C balance for broadleaves and conifers, re-spectively. The resulting net sink for CO2 in existing forests in 1990 was around 3,000 Gg CO2 yr-1 for the period 1990-1999 and somewhat higher (around 3,500 Gg CO2 yr-1) for the period 2000-2004. In the years 2000 and 2005 the sink was much lower than in all other years due to storms. The windthrow in Dec. 1999 made the harvested amount of wood in 2000 more than two times higher than during an average year. The storm-felled amount of wood amounted to 3.6 M m3 distributed over about 20,000 ha (Larsen and Johannsen, 2002). I Jan. 2005 a less severe storm also increased the annual harvest significantly compared to “normal” years.
For 2000-2005, the gross uptake of CO2 was slightly higher than for 1990-1999. This is mainly attributed to the higher number of respon-dents to the questionnaire, i.e. the included forest area was larger (440,000 ha vs. 411,000 ha in 1990. Annual gross increment per ha was similar for the two periods (11 m3 ha-1 y-1). The estimated increment in the period 2000-2005 was adjusted in order to account for the forest damage and changed age distribution caused by the storm in Decem-ber 1999. Gross increment and consequently gross carbon uptake was negatively affected by the windthrow as the age distribution changed towards low productive reforested stands. The loss of increment is es-timated at 182,000 m3 yr-1 for the period 2000-2009.
Indicator 2003 2004 2005
Area, ha 440800 440800 440800
Annual increment of stands, m3 ha-1 10.6 10.6 10.6
Annual increment of growing stock (merchantable), m3 4796474 4796474 4796474
Annual biomass growth of growing stock, t dm 2080298 2080298 2080298
Annual total biomass growth, t dm 3318133 3318133 3318133
C uptake, t 1659066 1659066 1659066
CO2 uptake, t 6083242 6083242 6083242
Annually harvested, m3 2126607 2205006 3523824
Annually harvested merchantable biomass, t dm 890982 916526 1417814
Annually harvested total biomass, t dm 1449985 1503806 2405925
Annual loss of C with harvested wood, t 724993 751903 1202962
Annual loss of CO2 with harvested wood, t 2658306 2756977 4410862
Net annual uptake of CO2, t 3424936 3326265 1672380
265
�� ����� Data on gross uptake of CO2, loss of CO2 due to harvesting (Figure 7.2) and the resulting net annual sink for CO2 for the period 1990 – 2005 in forests that existed before 1990.
1990 1992 1994 1996 1998 2000 2002 2004 2006
year
-1000
0
1000
2000
3000
4000
5000
Gg
CO
2
Net C uptakeHarvested CGross C uptake
Broadleaves
1990 1992 1994 1996 1998 2000 2002 2004 2006
year
-1000
0
1000
2000
3000
4000
5000 Conifers
���������� The C balance (in Gg CO2) for broadleaves and conifers in forests planted before 1990. The windthrow incidents for conifers during the storms on Dec. 3, 1999 and Jan. 8, 2005 are clearly visible in data for 2000 and 2005, respectively.
��������+����:����������3�������������������������3��������In 1989 the Danish Government decided to encourage a doubling of the forested area within a tree generation of approximately 80–100 years (Danish Forest and Nature Agency 2000). In order to reach this target, an afforestation rate of roughly 4–5,000 ha yr-1 was needed, but
Gross uptake of CO2 (Gg yr-1) -5743 -5743 -5743 -5743 -5743 -5743 -5743 -5743 -5743 -5743
Loss of CO2 in harvested wood (Gg yr-1)
2911 2732 2746 2534 2651 2761 2695 2614 2464 2476
Net annual sink for CO2(Gg yr-1) -2832 -3012 -2997 -3210 -3092 -2982 -3048 -3129 -3279 -3268
GREENHOUSE GAS SOURCE AND SINK CATEGORIES
���� ��� ��� ��� ���� ���
����������
Gross uptake of CO2 (Gg yr-1) -6083 -6083 -6083 -6083 -6083 -6083
Loss of CO2 in harvested wood (Gg yr-1) 5489 2618 2358 2658 2757 4411
Net annual sink for CO2 (Gg yr-1) -594 -3465 -3725 -3424 -3326 -1672
266
in reality the afforestation rate has been much lower with an average afforestation rate of 1935 ha yr-1 for the period 1990-2005. Afforestation is carried out on soils formerly used for agriculture (cropland). The annually afforested area is specified in Table 7.6. Data on the area af-forested by state forest districts, other public forest owners and private land owners receiving subsidies is derived from an evaluation report on afforestation (National Forest and Nature Agency, 2000). Area data for the years 1999-2005 is obtained from the records of the Danish For-est and Nature Agency. The area afforested by private land owners without subsidies is estimated by subtracting the afforestation catego-ries mentioned above from the total area afforested per year in the pe-riod 1990-99 as recorded in the latest Forestry Census (Larsen and Jo-hannsen, 2002). The Forestry Census included Nordmann’s fir planta-tions for Christmas trees and greenery on arable land as afforestation. These stands made up 40% of the total area afforested in the period 1990-99. However, the Nordmann’s fir plantations were not included in the reported afforested area. The reason for this is firstly that Nordmann’s fir plantations seldom become closed forest as the trees are harvested within a ten year rotation, and secondly changes in the market for Christmas trees may force land owners to revert the land use to agriculture after a few years.
�� ����� Distribution of afforestation area (ha) on different landowners and tree species. Plantations of Nordmann’s fir for Christmas trees and greenery are not included in the afforested area.
The approximate distribution of broadleaved and coniferous tree spe-cies is obtained from the Forestry Census of 2000 (Larsen and Johann-sen, 2002) for all ownership categories except private landowners re-ceiving subsidies. The tree species distribution for the latter category was obtained from the evaluation report on afforestation (Danish For-est and Nature Agency, 2000).
Full carbon accounting is used in a manner by which C-stock changes are based on area multiplied by uptake. Uptake is calculated using a simple carbon storage model based on the Danish yield tables for Norway spruce (representing conifers) and oak (representing broad-
Private forests with subsidies 1764 1288 1497 1537 463 2454
Private without subsidies 611 611 611 611 611 611
Total area 2753 2133 2343 2531 1325 3341
Broadleaved 2115 1577 1828 1975 857 2841
Coniferous 638 556 515 556 468 500
267
leaves) (Møller 1933). The yield tables used for calculation of carbon stores are valid for yield class 2 (on a scale decreasing from 1 to 4). No distinction is made between growth rates on different soil types. Growth rates are usually relatively high for afforested soils in spite of different parent materials (Vesterdal et al., 2007). This is due to the nu-trient-rich topsoil, which is a legacy of former agricultural fertilization and liming. The amounts of carbon sequestered in annual cohorts of afforested areas are summed up in the model to give the total carbon storage in a specific year (see Appendix A2).
The reason for the use of a different methodology for carbon seques-tration following afforestation is partly historical. Estimation of C se-questration for afforested lands started in a period with no previous data from a Forestry Census, and it has been maintained to keep a consistent time-series. However, the yield tables used for growth esti-mates are similar for forests existing before 1990 and afforestation since 1990. When the new NFI and new growth models are introduced in a few years (see 7.2.6), it is considered to further harmonize the cal-culation methods.
Wood volumes are converted to carbon stocks by the same method as for forests existing before 1990 except that a higher expansion factor of 2 is used for both species categories. The higher expansion factor is used in recognition of the age-dependency of expansion factors. The stem biomass represents a much lower proportion of the total biomass for age classes 1-10, thus a higher expansion factor is needed. How-ever, studies in other countries indicate that an expansion factor of 2 clearly underestimates the total biomass for age classes 1-10 (Schöne and Schulte, 1999). As there are no Danish expansion functions includ-ing age, it was chosen to use an expansion factor of 2 as a conservative estimate so far. This is obviously an area in need of improvement.
So far, there have been no thinning operations in the stands afforested since 1990; thus there are no reported emissions of carbon so far. How-ever, decomposition rates for the various slash components following harvesting are included in the model and these dynamics can be in-cluded when stands reach the age of first thinning. The first thinning operations in the model are done at the age of about 15 years for coni-fers and 25 years for oak. Carbon storage in wood products may be in-cluded in the accounting by use of a module with turnover rates for the various wood products. This option was not included in the calcu-lations of the figures presented here. For more information see Danish Energy Agency (2000).
Soil carbon pools have not been included in the model so far. Based on studies of soils in chronosequences of afforested stands, no significant changes in soil organic matter was expected to take place during the first 30 years following afforestation (Vesterdal et al., 2002). However, results from an EU project (http://www.sl.kvl.dk/afforest/) indicate that this may not be the case following afforestation on other soil types (Vesterdal et al., 2007). There is currently no systematic data available to explore this further.
The annual CO2 uptake and the cumulated CO2 uptake and afforested area since 1990 are given in Table 7.7 and the accumulated afforesta-
268
tion area and the annual CO2 uptake is given for broadleaved and co-niferous species separately in Fig. 7.4. As shown in Table 7.7, annual sequestration of CO2 in forests established since 1990 has gradually in-creased to 151 Gg CO2 in 2005, for further details see Annex A2. The annual CO2 sequestration will increase much more over the next dec-ades when cohorts of afforestation areas enter the stage of maximum current increment.
Table 7.7 Annual CO2 uptake, cumulated CO2 uptake and cumulated afforested area (ha) due to afforestation activities 1990 – 2005.
Cumulated CO2 uptake (Gg) -204 -277 -366 -473 -597 -748
Cumulated afforestation area (ha)
19292 21425 23768 26299 27624 30965
269
���������� a) The cumulated afforested area of broadleaves and conifers and b) the annual contribution of broadleaves and conifers to the afforestation C sink (in Gg CO2
yr-1).
During the Kyoto commitment period 2008–2012 (5 years), it is esti-mated that the Danish afforestation activities will result in sequestra-tion of 1,375 Gg CO2. This amount of C results from the afforestation of 48,000 ha of former arable land over the period 1990–2012. The sink capacity is based on a conservative estimate of approximately 2,500 ha of land afforested annually in the period 2006-2012, but it is possible that other instruments in addition to subsidization will make it possi-ble to increase the rate of afforestation and eventually the sequestra-tion of CO2.
%���������3���������������Table 7.8 shows the figures reported in this NIR report distributed to the land uses ������������� and ������������������������������. Afforestation currently contributes little to the total uptake in forestry, but the an-nual uptake increases as stands enter the stage of maximum rate of in-crement and as the afforestation area gradually increases.
�� ����� Annual CO2 uptake in total forest area, forests planted before 1990 and in afforestation of former arable land during 1990-2005.
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�������������������$��������2��In response to previous reviews the probably high but currently un-known uncertainty for CO2 uptake in forestry is discussed. Uncer-tainty will be addressed for the inventory data in detail when the first results from the new sample-based National Forest Inventory are available during 2007.
So far, the design of the currently used Danish Forestry Census has not made it possible to quantitatively address uncertainty of inventory data used to estimate the reported sink for CO2 in Danish forests. The uncertainty of the volume and increment estimates in the Forestry Census 1990 and 2000 are related to a number of issues: The values of
Total forest area (Gg CO2 yr-1) -653 -3539 -3813 -3532 -3450 -1823
Forests remaining forests (Gg CO2 yr-1)
-594 -3465 -3725 -3424 -3326 -1672
Afforestation since 1990 (Gg CO2 yr-1)
-59 -74 -88 -108 -124 -151
( % of total) 9.0 2.1 2.3 3.1 3.6 8.3
270
site productivity refer to fully stocked stands with no border effects and with a given thinning regime. However, a number of these issues are uncertain. The stands are not fully stocked as the estimates are based on 90% stocking but it may be lower. The very fragmented shape of the Danish forest area results in many borders and hence a reduction in the actual productivity on the area as a whole. Further-more, the yield table functions are based on a certain frequency of thinning, which in turn affect the standing volume. With the changing conditions for the forestry sector, these prescriptions are not followed, which in turn may lead to deviations, both positive and negative, from the estimated volume and increment. Further details and alternative estimates can be found in Johannsen (2002) and Dralle et al. (2002).
Other factors also contribute to uncertainty of the reported sinks. As previously mentioned, the lack of national biomass expansion factors or better expansion functions makes the calculation step from biomass to total biomass the most critical in terms of uncertainty. Basic densi-ties of wood from different tree species are better documented and the C concentration is probably the least variable parameter in the calcula-tions.
In recognition of the difficulties in analyses of uncertainty, the esti-mated uptake of CO2 in the forestry sector must be treated with cau-tion. However, the assessment of uncertainty will improve signifi-cantly after 2007 when the new National Forest Inventory can supply the first national estimate of stocks of wood, increment and harvest based on a design with permanent sampling plots and partial re-placement. The new design will enable an assessment of uncertainty related to inventory data.
%����������������������The forest area in 1990 and 2000 was not the same for forests existing before 1990 (411,000 and 440,000 ha, respectively). This is due to the nature of the Forestry Census, i.e. there were different numbers of re-spondents in 1990 and 2000. We are aware that this is a problem. The difference in gross uptake of CO2 between 1990-1999 and 2000-2005 is almost solely due to the difference in numbers of respondents to the questionnaire (i.e. forest area) as annual gross increment per ha was similar for the two periods. However, as mentioned below (Section 7.2.6), we prefer to avoid recalculations of the present data based on the Forestry Census due to the coming large data revision based on the new National Forest Inventory.
In addition to this coming revision, we are currently initiating work on reconstruction of the land use matrix for 1990 (databases, remote sens-ing data and aerial photos). We plan to elaborate forest maps for 1990 and 2005 and the project will also outline a procedure for updating of these maps. This is necessary in order to be able to apply the same for-est definition (FAO-TBFRA) in 1990 as that used in the commitment period.
"����� (������$�����������������������������
QA for the area of existing forests is carried out by Statistics Denmark, and QA for afforestation area is mainly carried out by the Danish For-
271
est and Nature Agency, as this organisation is responsible for the ad-ministration of subsidies. Harvesting data to support estimates of emissions from forests existing before 1990 are derived from Statistics Denmark. The QA of harvesting data is therefore placed under QA within Statistics Denmark. Spreadsheets are in secure files at Danish Centre for Forest, Landscape and Planning.
"����� (������$���������������������
Since the submission to UNFCCC in April 2006 no methodological re-visions have been carried out, but this section has been amended with more information (e.g. Table 7.4).
"��� � (������$�������$���������$���������
%����,�4�������'�����/��������The most important improvement for the reporting of the source cate-gory Forest Land was the initiation of the new sample-based National Forest Inventory (NFI) in 2002. The NFI will replace the Forestry Cen-sus as source of activity data and removal data. Statistics Denmark is still expected to supply background data for emission (harvesting) but those data can be combined with harvesting data from the NFI.
The mission of the NFI is, as stated in the Forest Act of Denmark, to improve the understanding and management of the Danish Forests by maintaining a comprehensive inventory of their status and trends. The objectives of the inventory are to require information on wood volume by tree species and diameter class, area estimates of forest land by type, stand size, ownership, site quality and stocking. Additional in-formation like: changes in the forest area, growth, mortality, timber removals and measures for successful regeneration is also included in the inventory. The National Forest Inventory uses a continuous sam-ple based inventory with partial replacement of plots. The NFI system gives good estimates of both growth (permanent clusters) and current status (all clusters - including the temporary). The sampling of vari-ables must be economically feasible. The selected variables must cover the indicators concerning sustainable forest management and meet the data needs for national and international forest statistics.
The NFI was initiated in 2002 and has collected data on approximately 60% of the total number of sample plots. One fifth of the sample plots are visited every year. The fifth and last year of data collection (2006) has now been completed and the first year of the second full inventory is currently planned for 2007. Over the five years more than 7000 plots have been visited and inventoried by the 3 two-man teams travelling from May through September. Data will be prepared and analysed for a report during 2007.
/�$���������$�������3�������4'/����������������The background data to come from the sample-based NFI will provide much better estimates of the status of the forest area since 1990 and the development in forest area in the future. Furthermore, growth and harvesting estimates will be based on real sample plots, enabling quantification of error for background data used in calculation of car-bon stock changes.
272
As a first step, after the first full rotation (five years), the NFI is able to supply new activity data, whereas repeated measurements are neces-sary to assess carbon stock changes. Due to the continuous monitoring every year of one fifth of the sample plots, the first estimates of carbon stock changes may possibly be obtained following just one or two years of measurements in the second rotation of the NFI.
The NFI also supports reporting of more carbon pools than previously. Coarse woody debris and understorey vegetation is monitored and carbon stock changes will be estimated. Unfortunately soil sampling has not been included as part of the NFI so far. However, simple measurements of forest floor thickness in each plot enable estimation of carbon stock changes in the litter pool according to IPCC GPG. Ex-isting national data on forest floor depth/mass relationships can be used for this purpose. Better information on C stock changes of Danish forest soils is foreseen for the commitment period. The main aim is to document that Danish forest soils are not a source for CO2 emissions.
For afforested cropland, the NFI will provide activity data for com-parison with the other data sources currently used (subsidized affore-station area). The NFI may have limitations in gauging the relatively small afforestation area. However, the NFI will provide a better esti-mate of the residual area of land afforested by private landowners without subsidies than the current estimate based on the Forestry Cen-sus.
A weakness in the Danish biomass carbon estimates is the lack of na-tional biomass expansion factors or functions. However, national data on aboveground biomass expansion functions for Norway spruce will be available within a couple of years. Data on belowground carbon is even scarcer. So far it has only been possible to conduct a pilot study in Norway spruce in a thinning trial at one site. However, root-top re-lationships from these stands will provide a better basis for selecting root-top relationships for Norway spruce from the literature. Within a year or so another project on root architecture will also contribute with expansion functions for belowground biomass for the four most com-mon Danish tree species.
In addition work has just been initiated on a reconstruction of the land use matrix by 1990 and 2005 by use of databases, satellite photos and aerial photos.
"��� ��$�����
The total Danish agricultural area of approximately 2.7 million hec-tares has been related to approximately 700,000 individual fields, which again is located at 220,000 land parcels. As mentioned in the overview a detailed GIS analysis has been performed on the agricul-tural area with data on land use in 1998. With data from EUs IACS (In-tegrated Administration and Control System), the EUs LPIS (Land Parcel Information System) and detailed soil maps (1:25,000) a GIS analysis has been made. This gives an average field size of less than four hectares. The actual crop grown in each field is known from 1998 and onwards. However, for simplicity the distribution between min-
273
eral soils and organic soils is kept constant for all years from 1990 to 2005.
"���*� (��������� ���������$����
The main sources/sinks on Cropland are land use, establishing of hedgerows and liming. Table 7.9 shows the development in the agri-cultural area from 1990 to 2005 (Statistics Denmark). In Denmark a continuous decrease of 10-12,000 hectares per year in the agricultural area is observed. A part of the area is used for reforestation, settle-ments, nature conservation etc., but no clear picture is available yet.
�� ����� Agricultural areas in Denmark 1990-2003, hectare.
1CM refers to that the area is treated under Cropland Management. GM refers to Grassland Management.
"����� ����� ������������
Based on the GIS analysis on the Land Parcel Information from 1998 is the agricultural area distributed between mineral soils and organic soils and subdivided into cropland and permanent grassland. Table 7.10 and 7.11 shows the main result from the GIS analysis. It can be seen, as expected, that set-a-side, grass in rotation and permanent grass is more common on organic soils than on mineral soils. The per-centage distribution in Table 7.12 is used as parameters when estimat-ing the land use between different categories for all years between 1990 and 2005.
Total 2648251 2674015 2665555 2658274 2645304 2589347
�� ������ The distribution of crops between organic and mineral soils in 1998 according to the GIS-analysis. The figures are given in hectares. The figures are slightly different from Table 7.9 due to dif-ferent data sources.
Soil type Annual crops in rotation Set-a-side Grass in rotation Permanent grass Total
Organic 82191 16056 24885 27864 150997
Mineral 2098396 126777 214053 114944 2554169
Total 2180587 142833 238938 142808 2705166
274
�� ������ The distribution of organic soils and mineral soils in per cent in 1998.
Soil type Annual crops in rotation Set-a-side Grass in rotation Permanent grass Total
�� ����� The percentage distribution of the agricultural area used in the emission model.
Soil type Annual crops in rotation Set-a-side Grass in rotation Permanent grass
Organic 3,8% 11,2% 10,4% 19,5%
Mineral 96,2% 88,8% 89,6% 80,5%
Total 100,0% 100,0% 100,0% 100,0%
Furthermore the organic soils are divided in shallow and deep organic soils. 38% of the organic soils are according to the Danish soil classifi-cation deep organic soils (Sven Elsnap Olesen, DIAS, pers. comm).
The emission factors for organic soils are shown in Table 7.13 Negative values indicates a built up of organic matter. Wet organic soils are de-fined as having a water table between 0 and 30 centimetres. The car-bon dioxide emission factor from the organic soils is based on emis-sion data from Denmark, UK, Sweden, Finland and Germany, ad-justed for differences in annual mean temperature to the average Dan-ish climate (Svend E. Olesen, DIAS, 2005). E.g. data from southern Finland are adjusted with a factor of 2 and data from central Germany with a factor of 0.6.
Emissions of nitrous oxide from organic soils are estimated from deg-radation of organic matter and the C:N-ratio in the organic matter. Figure 7.5 shows the C:N-ratio for 160 different soils. Hence for or-ganic soils are used a C:N-ratio of 20. As emission factor is used the IPCC Tier 1 value of 1.25%.�
�� ������ Emission factors for organic soils. Negative values indicates a built up.
Emission factor, t C ha-1y-1
% organic
soils1 % with deep organic soils
% wet soils
Dry
shallow
Dry
deep
Wet
shallow
Wet
deep
Annual crops 3.8 38 0 5 8 0 0
Grass in rotation 11.2 38 0 5 8 0 0
Set-a-side 10.4 38 26 3 4 -0.5 -0.5
Permanent grass (drained) 19.5 38 26 3 4 -0.5 -0.5 1Percentage of the total area from the annual survey from Statistics Denmark classified as organic
275
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A 3-pooled dynamic soil model has been developed (Petersen 2003, Petersen et al. 2002, 2005, Gyldenkærne et al. 2005) to calculate the soil carbon dynamics in relation to the Danish commitments to UNFCCC. C-TOOL is used for both cropland and grassland. Due to the frag-mented Danish landscape with small areas with permanent grassland, changes in C stock in grassland are included in the emission from Cropland (5.B). C-TOOL is run on a county based level (average 250,000 hectares), where all different crops grown in that area are taken into account, annual reported crop yield, the amount of crop residues returned to soil (data from Statistics Denmark), roots, amount of solid manure and slurry in the specific county based on output from the DIEMA-model (see the agricultural sector) for the different coun-ties. C-TOOL is a 3-pooled dynamic model, where the approximate average half-live times for the three different pools are 0.6-0.7 years, 50 years and 600-800 years. The main part of biomass returned to soil each year is in the first and easiest degradable pool. C-TOOL is pa-rameterised and validated against long-term field experiments (100-150 years) conducted in Denmark, UK (Rothamsted) and Sweden and is “State-of-art”.
The Danish soil classification is divided in mineral soils and organic soils. Danish organic soils are defined as soils having >10% SOM in contradiction to the IPCC definition where organic soils has >20% SOM. The modelling with C-TOOL is performed under the assump-tion that the soils above 10% SOM, but below 20% SOM can be treated as mineral soils. In most models this may lead to overestimated decay rates, but as the realized decay of the utilised model falls with rising C to N ratio, the decay rate presumably is within realistic boundaries also for the mineral soils with high SOM content. This matter should be investigated further though.
C-TOOL is initiated with data from 1980 and run multipliable times until stability before the emissions from 1980 and onwards was calcu-lated. As temperature driver is used actual monthly average tempera-
y = 0,3575x + 15,448
0,00
5,00
10,00
15,00
20,00
25,00
30,00
35,00
40,00
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50,00
55,00
60,00
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Organiske jordeSpagnumarealer
���������� C:N-ratio in organic soils in relation to soil carbon content (Olesen 2004)
276
tures. In Figure 7.6 and Table 7.14 is shown the calculated emissions from 1980 to 2005.
The main drivers in the degradation of soil biomass are temperature and humidity. The Danish climate is quite humid with winter tem-peratures around zero degrees Celsius and hence is the importance of soil humidity on the model outcome low in contradiction to tempera-ture which has a high effect on the emission. As mentioned is the ma-jor part of the biomass returned to soil quite easily degradable. Warm winters with unfrozen soils in connection with high inputs of biomass will therefore as a result yield high emissions from the soil compared to more cold years which will yield low emissions. E.g. are the peaks in 1990, 1998 and 2000 due to high harvest yields and normal tempera-tures, whereas the peak in 1993 in figure 7.6 is due to a normal harvest year but very low temperatures with low degradation rates. However, the modelled emissions are found to be the most realistic emissions es-timates for Denmark. In the most recent years (1999-2005), there have been very warm winters in Denmark and hence is the modelled CO2-emission from the mineral soils are quite high in these years and higher than expected if having used average standard temperatures for 1961-90. If average temperatures were used the model calculation shows an increase in the soil C stock in 1999-2005.
As described in the agricultural sector has the Danish farmers faced increased demands for lower environmental impact since the mid 1980’ies. This includes, among other, ban on field burning and in-creased demands for winter green crops (winter cereals and autumn sown catch crops such as grass and rape) to reduce leaching of nitro-gen and ban on autumn application of animal manure. This change in agricultural praxis has influence on the C stock in soil in the longer term. The general effect on the C stock in soil is that the 1980’ies shows a decrease in the C stock. In the 90’ies the C stock seems to have been stabilised and in future a small increase in the C-stock is expected, al-though it depends on how big the global warming will be in near fu-ture.
-1500
-1000
-500
0
500
1000
1500
2000
1980 1985 1990 1995 2000 2005
6 ����
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Mineral soils, 5 years average
Mineral soils, annual change
���������� Modelled total annually emission and five-year average from all mineral soils in Denmark, Gg C/yr from 1980 to 2005.
277
�� ������ Modelled carbon stock (0-100 cm) in mineral soils from 1980 to 2005.
abased on projected C input and climatic conditions for 2006 and 2007.
In Table 7.14 was shown the modelled annual emissions and five-year average. To reduce the interannual variability in the reporting to UNFCCC is used the recommended five-year average (IPCC, 2004, Section 4.2.3.7 p 4.23).
No formal uncertainty assessment has been made so far.
A national Danish soil sampling program was initiated in 1987 on app. agricultural 380 fields scattered throughout Denmark on all soil types. Resampling was made in 1998. Resampling will take place 2007 and in 2012 to verify the model predictions. From 1987 to 1998 a decrease in soil C was found on pig farms and on farms without animal hus-bandry. On cattle farms an increase in soil C was registered, probably due to high manure application rates and a high percentage of grass in the rotation (grass has a large amount of root residues). An up scaling to the whole Danish area yields a very uncertain and not significant increase in soil C of two ton C/ha from 110 ton/ha to 112 ton/ha (0-50 cm) in the same period, indicating that the output from C-TOOL is in line with the soil samples.
Year Carbon stock,
Gg C
Emission,
Gg C/yr
Emission,
Five-year average,
Gg C/yr
1980 431,297071
1981 430,765166 0,531905
1982 429,874044 0,891122
1983 428,696583 1,177461 0,4653804
1984 429,127762 -0,431179 0,4242988
1985 428,970169 0,157593 0,3446512
1986 428,643672 0,326497 0,2892132
1987 428,150788 0,492884 0,5283182
1988 427,250517 0,900271 0,3931418
1989 426,486171 0,764346 0,3050134
1990 427,00446 -0,518289 0,4197582
1991 427,118605 -0,114145 -0,1167984
1992 426,051997 1,066608 -0,0414664
1993 427,834509 -1,782512 0,107693
1994 426,693503 1,141006 -0,0316664
1995 426,465995 0,227508 -0,09099
1996 427,276937 -0,810942 0,0918138
1997 426,506947 0,76999 0,0967486
1998 427,37544 -0,868493 -0,006296
1999 426,20976 1,16568 0,1956316
2000 426,497475 -0,287715 0,255924
2001 426,298779 0,198696 0,4051602
2002 425,227327 1,071452 0,1536626
2003 425,349639 -0,122312 -0,045556
2004 425,441447 -0,091808 -0,228184a
2005 426,069934 -0,628487 -0,244708a
278
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Organic soils are defined as having >20% OM. The emission from or-ganic soils is estimated from the actual land use of the organic soils in four groups: annual crops, set-a-side, grass in rotation and permanent grassland. Only emission from organic soils on grassland is reported under grassland (Table 5.C). Emissions from grassland on mineral soils are calculated with C-TOOL and included in Cropland.
The estimated emissions from organic soils are given in Table 7.15. The approximately area distribution are shown in Table. 7.10 and the emis-sion factors are given in Table 7.13. For 1990 to 2005 the different classes are given as a fixed percentage of the total annual area from Statistics Denmark. The differences between years are due to inter-annual changes in the area given by Statistics Denmark.
�� ������ Emissions from organic soils 1990 to 2005
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Permanent horticultural plantations are reported separately under Cropland (Table 5.B). Permanent horticulture is only a minor produc-tion in Denmark. The total area for different main classes is given in Table 7.16. Due to the limited area and small changes between years the CO2 removal/emission is calculated without a growth model for the different tree categories. Instead the average stock figures are used in Table 7.16 multiplied with changes in the area to estimate the an-nual emissions/removals. Perennial horticultural crops account for approximately 0.07% of the standing C-stock.
The factors for estimating the C-stock in perennial horticulture are given in Table 7.17. Expansion factors and densities are the same as used in forestry (Section 7.2).
�� ������ Area with perennial fruit trees and – bushes, C stock and stock changes from 1990-2003.
�� ������ Parameters used to estimate the C-stock in perennial horticulture (Gyldenkærne et al. 2005).
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Since the beginning of the early 1970s governmental subsidiaries have been given to increase the area with hedgerows to reduce soil erosion. Annually financial support is given to approximately 600-800 km of hedgerow per year. Only C-stock changes in subsidised hedgerows are included in the inventory, not private erections. In 1990 75% of the old single-rowed Sitca-spruce hedgerows were replaced with 3- to 6-rowed broad-leaved hedges. In 2004 only 20% is replacements and the remaining is new hedges cf. Table 7.18. The figures are converted from kilometres to hectares according to the type of hedgerow. A simple linear growth model has been made to calculate the sink/removal from hedgerows. The parameters are given in Table 7.19. New hedge-rows account for approximately 0.7% of the standing accounted C-
Biomass, t ha-1 29.32 25.14 11.64 10.93 20.52 4.19 4.19
C content, t C t-1 biomasse-1 0.50 0.50 0.50 0.50 0.50 0.50 0.50
C, t ha-1 14.66 12.57 5.82 5.46 10.26 2.09 2.09
C, t ha-1 (average) 13.61 5.64 10.26 2.09 2.09
280
stock. In 1990 there was a net emission because the removed hedge-rows were 12-15 meters tall Sitca-spruce. From 1994 there has been a net sink in the new hedgerow due to increasing area and the decreas-ing replacing rate.
�� ������ Areas with new hedgerows, C stock and stock changes 1990-2005. (De danske Plantningsforeninger, 2004 and update).
�� ������ Parameters used for estimation of C in hedgerows (De danske Plantnings-foreninger, 2004)
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The area with grassland is defined as the area with permanent grass given in the annual census from Statistics Denmark (Table 7.9). In 2005 176,647 hectares is reported as permanent grassland. Based on the GIS analysis it is concluded that 16,482 hectares are organic and the re-maining grassland is on mineral soils. Emissions/sinks from grassland on mineral soils are included in cropland mineral soils. For the organic
Sink in new hedge, Gg C y-1 -22 -24 -25 -27 -28 -30 -32 -34 -35 -37
Stock in new hedges, Gg C 155 179 204 231 259 289 320 354 389 427
Greenhouse gas source and sink categories
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Replaced, % 27 25 23 22 20 20
Replaced, km 292 298 63 187 110 101
Replaced, ha 73 74 16 47 28 25
New hedges, ha 626 682 207 474 320 287
Removed hedge incl. thinning, Gg C y-1
12 12 3 8 5 19
Sink in new hedge, Gg C y-1 -39 -41 -42 -43 -44 -45
Stock in new hedges, Gg C 466 507 549 592 640 674
Old hedges
(1-row.)
New hedges
(3-6 row.)
Wooden Stock, m3 ha-1 480 260
Density, broad-leaved 0.56 0.56
Density, spruce 0.37 0.37
Density used in the calculations 0.38 0.50
Above ground biomass, m3 ha-1 182 130
Expansion factor 1.80 1.20
Biomass, m3 ha-1 328 156
t C t biomass-1 0.50 0.50
t C ha hedgerow-1 164 78
Year from plantation to first thinning - 25
Thinning per cent - 45%
Year between thinning - 10
281
soils a CO2-emission from drained areas with a water table below 30 cm is assumed. For areas with a water table between 0 and 30 cm a built up of organic matter is assumed (Table 7.13).
In Table 7.15 were the annual emissions given for grassland on organic soils. The emission from grassland is reduced from 25.3 Gg CO2-C in 1990 to 20.6 Gg in 2005 due to a reduced area with permanent grass.
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Wetland includes land for peat extraction and re-established anthro-pogenic wetlands. Naturally occurring wetlands are not included in the inventory.
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The area with peat extraction in Denmark is rather small. In 1990 the open area was estimated to 1,067 hectares decreasing to 885 hectares in 2005. All areas are nutrient poor raised bogs. The emission from the open area is calculated according to the standard approach for nutri-ent poor areas with an emission factor of 0.5 t C ha-1 y-1. Because the underlying default factor is mainly based on Finish data, a higher emission factor than recommended is chosen. This is in accordance with the difference in temperatures between Denmark and Finland (see Section 7.3). The nitrous oxide emission from peat land is esti-mated from the total N-turnover multiplied with a standard emission factor of 1.25%. The C:N-ration in the peat is estimated to 36 in an analysis from the Danish Plant Directorate (PDIR 2004). Hence the N2O emission is estimated to 0.546 kg N2O per t C. Due to changes in the Reporter in 2007 is it now possible to include a reduced CH4-emission from the drained wetlands. This is included for all years from 1990 with a factor of 20 kg CH4 ha-1 y-1.
�� ����� Annual emissions from the surface area where peat extraction takes place, Gg C y-1 and N2O y-1.
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In order to reduce leaching of nitrogen to lakes, rivers and coastal wa-ters Denmark has actively re-established wetlands since 1997. In total 541 different areas ranging from 0.1 hectare up to 2,180 hectares has been reported to NERI. The total area converted to wetlands up to the year 2003 is 4,792 hectares and 3,767 hectares with raised water table. In 2004 1,622 hectares re-established wetlands and 318 hectares with raised water tables were reported. In 2005 only 413 hectares was re-
established and no areas with raised water table. The area with raised water table will be unsuitable for annual cropping and protected by the legislation against future changes. Figure 7.7 shows the distribu-tion of the areas in Denmark in 2003.
For areas converted before 2004 is a detailed vector-map available. Due to the reconstruction on the Danish municipalities will all GIS maps in future be collected by a single unit. When the reconstruction is complete an updated map will be constructed. The GIS-analysis shows that only part of the area is on former cropland and that the distribu-tion between mineral and organic soils differs (Table 7.21 and 7.22). In wetlands 68% of the area is on former cropland or grassland and in the areas with raised water table 81% is on former cropland or grassland. Furthermore it can be seen that there is a higher percentage of grass-land in the areas with raised water table besides that these areas have a higher percentage with organic soils. Only the areas with annual crops, set-a-side, grass in rotation and permanent grassland are in-cluded in the emission estimates in the inventory. The parameters used to estimate the emission are given in Table 7.13.
���������� Areas with established wetlands and increased water tables from 1997 to 2003.
283
�� ���� Area classification where the water table has been raised in hectares.
The net-accumulation of C, with a standard sink factor of 0.5 t C ha y-1 for the former agricultural area is included in the CRF-Table 5.D. The total annual net - build up from anthropogenic wetlands in 2005 - is es-timated to 3.99 Gg C (only former cropland and grassland is included) (Table 7.23). The decreased oxidation of organic matter of the organic soils (due to the re-wetting) is included in Table 5.B and 5.C as a de-creased total area. Until a full matrix for the Danish area is performed there will be some inconsistency in the total area.
�� ����� Increase in carbon sink in anthropogenic established wetlands, 1990-2004, Gg C y-1.
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C-stocks in settlements are not estimated. The annual changes in C-stock in settlements are assumed to be negligible, but because no esti-mates have been made it are reported as NE in the CRF Table 5.E.
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C-stocks in other types of land are not estimated. The annual changes in C-stock in other types of land are assumed to be negligible, but be-cause no estimates have been made it are reported as NE in the CRF Table 5.F.
�� ����� Area classification of the established wetlands in hectares.
Net sink. Gg G y-1 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 -0.04 -0.16
Greenhouse gas source and sink categories
���� ���� ��� ���� ���� ����
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Net sink. Gg G y-1 -1.34 -1.80 -2.35 -3.17 -3.84 -3.99
284
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Liming of agricultural soils has taken place for many years. The Dan-ish Agricultural Advisory Centre (DAAC) has annually published the lime consumption for agricultural purposes since 1960 (Table 7.24). DAAC are collecting data from all producers and importers. By legis-lation all producers and importers are forced to have their products analysed for acid neutralisation content. The analysis is carried out by the Danish Plant Directorate and published annually (PDIR 2004). The published data from DAAC are corrected for acid neutralisation con-tents for each product and thus given in pure CaCO3. For that reason there is no need to differ between lime and dolomite as made in the guidelines, as this has already been included in the background data. The data from DAAC includes all different products used in agricul-ture, including e.g. CaCO3 from the sugar refineries.
The amount of lime used in private gardens has been estimated from the main supplier to private gardens. According to the company (Kongerslev Havekalk A/S, pers. comm.) they are responsible for 80% of the sale to private gardens. Their sales figures have been used to es-timate the total consumption in private gardens. Furthermore the fig-ures are corrected for acid neutralisation capacity according to the data from the Danish Plant Directorate. This gives an approximate amount of 2,300 t CaCO3 y-1 in private gardens. This figure has been used for all years.
Only a very little amount of lime is applied in forests (<0,5%) and on permanent grassland. Therefore all liming is included in the inventory under cropland (CRF Table 5(IV). The amount of C is calculated ac-cording to the guidelines where the carbon content is 12/100 of the CaCO3. It is assumed that all C disappear as CO2 the same year as the lime is applied.
The amount of lime used for agricultural purposes has declined with 70% since 1990. From 2004 to 2005 the consumption in agriculture has slightly increased from 356 t CaCO3 to 497 t. 500 t CaCO3 is expected to be the lowest consumption needed to maintain appropriate pH values in the Danish agricultural soils at the moment. This main reason for the reduced lime consumption is a decreased need for acid neutralisa-tion due to less SOx deposition in Denmark and a reduced consump-tion of fertilisers containing ammonium. The inter-annual variation is primarily due to weather conditions (if it is possible to drive in the fields) and the economy in agriculture.
285
�� ����� Lime application on cropland and grassland and in forests, 1990-2004.
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A Tier 1 uncertainty analysis has been made for part of the LULUCF sector cf. Table 7.25 The uncertainty in the activity data is rather low. The highest uncertainty is associated to the emission factors. Espe-cially the emission/sink from mineral soils and organic soils has a high influence on the overall uncertainty. Because there is used a dy-namic soil model to calculate the emission/sink from mineral soils where the emission is averaged for five years it makes no sense to cal-culate an annual uncertainty for this source.
The LULUCF sector contributes to a large part of the total estimated uncertainty.
�� ����� Tier 1 uncertainty analysis for LULUCF for 2005. No estimates are given for forestry.
Total, t CaCO3 592.3 455.9 530.0 514.3 358.3 499.3
Total, Gg C y-1 -71.1 -54.7 -63.6 -61.7 -43.0 -59.9
Emission/sink, Gg CO2-eqv.
Activity data, %
Emission factor, %
Combined uncertainty
Total uncertainty,
%
Uncertainty 95%, Gg CO2-eqv.
5.A Forests 1823.4 NE NE
Broadleaves, Forest remaining forest CO2 1026.7 NE NE NE
Conifers, Forest remaining forest CO2 645.3 NE NE NE
Broadleaves, Land converted to forest CO2 95.3 NE NE NE
Conifers, Land converted to forest CO2 56.1 NE NE NE
5.B Cropland -43.5 44.9 550.2
Mineral soils CO2 897.3 10 20 22.4 22.4 200.6
Organic soils CO2 -1003.0 10 50 51.0 51.0 511.4
Hedgerows CO2 151.0 5 20 20.6 20.6 31.1
Perennial horticultural CO2 -13.1 10 10 14.1 14.1 -1.9
5.C.Grassland -75.5 51.0 38.5
Organic soils CO2 -75.5 10 50 51.0 51.0 38.5
5.D Wetlands 13.4 56.0 7.5
Land for peat extraction CO2 -1.6 10 50 51.0 51.0 0.8
Land for peat extraction N2O -0.1 10 100 100.5 100.5 -0.1
Land for peat extraction CH4 0.5 10 100 100.5 100.5 0.5
Reestablished wetlands CO2 14.6 10 50 51.0 51.0 7.5
Liming CO2 -219.7 5 50 50.2 50.2 -110.4
286
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Recalculations have been made for mineral soils in 2003 and 2004 due to the chosen methodology where a five-year average is used. The area with re-established wetlands has been changed slightly due to new data. CH4-emissions from wetlands have been included for the whole time series.
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Lime in animal fodder will be incorporated in future. C-TOOL which estimates the emission from mineral soils in cropland and grassland will be validated further. A remapping of all organic soils, both agri-cultural and forest soils, will take place in 2007 to 2010 to get a better estimate of the actual area. Danish emission factors for CO2 and N2O from organic soils will be established in 2008-2010. A full land use ma-trix for 1990 and 2005 will be available next year.
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A general QA/QC plan for the land use sector is under development. For forestry the formal QA/QC plan is not yet implemented. This will take place in 2007. The following Points of Measures (PM) are taken into account.
The area estimates cropland and grassland are very precise due to un-restricted access to detailed data from EUs Integrated Administration and Control System (IACS) on agricultural crops on field level and the use of the vector based Land Parcel Information System (LPIS). This access includes both Statistics Denmark and NERI. The total uncer-tainty in the crop data is estimated by Statistics Denmark to be <0.5%. Together with detailed soil maps this gives a unique possibility to es-timate the agricultural crops on different soil types and hence track changes in land use. However, IACS and LPIS are only available from 1998 and onwards, and estimates for 1990 are therefore more uncer-tain. The QA of crop data is made by Statistics Denmark. Data on hedgerows are based on subsidised hedgerows and QA is carried out by “Landsforeningen af Plantningsforeninger” who is responsible for the administration of the subsidiaries. The uncertainty in the number of plants used for the hedgerows is not estimated but is assumed to be very low because of the subsidised system. The re-establishment of wetlands is based on vector maps received from every county in Denmark. The uncertainty is not estimated but assumed to be very low due to the subsidised system.
Emissions from areas other than forestry, cropland, grassland, peat mines and re-established wetlands are not included. Denmark still needs to make a full land use matrix from 1990 and onwards. This will be carried out in 2007 by analysing satellite data from the European Space Agency (ESA). Natural areas such as heath land, natural wet-lands etc. are thus not included in the inventory.
Data Storage
level 1
1. Accuracy DS.1.1.1 General level of uncertainty for every data set including the rea-soning for the specific values
287
The amount of lime used is more uncertain. Data is collected by DAAC from all suppliers and importers and published every year in “Planteavlsorientering.” The collected data is assumed to be very reli-able. No uncertainty analysis has been made, but it is assumed that it is in the range of 5-10%. The emission factor may be overestimated due to expected leaching of CO3- -, however no data are available on this issue.
A range of experts from the Faculty of Agricultural Sciences are re-peatedly involved in discussions and report writings on topics related to the inventory.
No comparison of the activity data with other countries has been made.
See DS.1.1.1
The original data files are stored at NERI in I:/rosproj/luft_emi-/inventory/2005/5_LULUCF/level_1a_storage/
Signed formal agreements on data delivery have been made with the Statistics Denmark, The Danish Plant Directorate, Danish Agricultural Advisory Centre and Faculty of Agricultural Sciences.
The signed formal agreements are stored in: I:/rosproj/luft_emi-/inventory/allyears
No formal agreement has been made with “Landsforeningen de Dan-ske Plantningforeninger” on data delivery. However, “Landsforenin-gen de Danske Plantningforeninger” are under public administration and thus are all data and maps directly available.
Data Storage
level 1
1. Accuracy DS.1.1.2 Quantification of the uncertainty level of every single data value including the reasoning for the spe-cific values.
Data Storage
level 1
2.Comparability DS.1.2.1 Comparability of the data values with similar data from other countries, which are comparable with Denmark and evaluation of discrepancy.
Data Storage
level 1
3.Completeness DS.1.3.1 Documentation showing that all possible national data sources are included by setting up the reasoning for the selection of data sets
Data Storage
level 1
4.Consistency DS.1.4.1 The origin of external data has to be preserved whenever possible with-out explicit arguments (referring to other PM’s)
Data Storage
level 1
6.Robustness DS.1.6.1 Explicit agreements between the external institution of data delivery and NERI about the condition of delivery
288
No formal agreements have been made with the Danish counties on data delivery for vector based field maps on re-established wetlands because this public sector is currently under reconstruction. This issue will be sought solved in 2007 and 2008.
Please refer to DS.1.1.1
Much documentation already exists in the literature list. A separate list of references is stored in I:/rosproj/luft_emi/inventory/2004/5_LU-LUCF/level_1a_storage/
0@������������������A�Statistics Denmark: Karsten K. Larsen ([email protected])
Landsforeningen De danske Plantningsforeninger: Helge Knudsen ([email protected])
In the uncertainty calculations is assumed a normal distribution of all activity data as well as for the emission factors. In many cases where data on emission factors are scare are the uncertainty based on expert judgement made by the involved institutions and persons.
The uncertainty assessment for LULUCF is given in the NIR except for forestry. This will be included next year when a full NFI has been made. In the documentation reports is normally given the size of the variation.
Data Storage
level 1
7.Transparency DS.1.7.1 Summary of each data set including the reasoning for selecting the spe-cific data set
Data Storage
level 1
7.Transparency DS.1.7.3 References for citation for any ex-ternal data set have to be available for any single number in any data set.
Data Storage
level 1
7.Transparency DS.1.7.4 Listing of external contacts to every data set
Data Processing
level 1
1. Accuracy DP.1.1.1 Uncertainty assessment for every data source as input to Data Storage level 2 in relation to type of variability (Distribu-tion as: normal, log normal or other type of variability)
Data Processing
level 1
1. Accuracy DP.1.1.2 Uncertainty assessment for every data source as input to Data Storage level 2 in relation to scale of variability (size of variation intervals)
289
The methodological approach is mostly scientifically state-of-art methods, however, in some cases are the IPCC emission factors chosen when it not has been possible to estimate more scientifically correct country specific values.
Emission factors and growth functions has only briefly been compared with IPCC guidelines.
The LULUCF inventory is made according to the IPCC-GPG on LU-LUCF, 2004.
Emissions and sinks in forest soils are not included in the inventory. Application for financial funding has been made to cover this area. It is assumed that data forest soils are available in 2008 or 2009.
Lime used in feeding stuff is not covered in the inventory, neither in the guidelines. Due to the high number of animals in Denmark with optimised feeding, large quantities of CaCO3 are applied to the soil through manure. A model for this is under development.
Natural habitats, natural wetlands, settlements and other land are not included in the inventory. At the moment no data are available.
In Denmark only very few data are restricted (military installations). Accessibility is not a key issue, it is more lack of data.
The calculation procedure is consistent for all years.
Data Processing
level 1
1. Accuracy DP.1.1.3 Evaluation of the methodological ap-proach using international guidelines
Data Processing
level 1
1. Accuracy DP.1.1.4 Verification of calculation results using guideline values
Data Processing
level 1
2.Comparability DP.1.2.1 The inventory calculation has to follow the international guidelines suggested by UNFCCC and IPCC.
Data Processing
level 1
3.Completeness DP.1.3.1 Assessment of the most important missing quantitative knowledge
Data Processing
level 1
3.Completeness DP.1.3.2 Assessment of the most important missing accessibility to critical data sources that could improve quantitative knowledge
Data Processing
level 1
4.Consistency DP.1.4.1 In order to keep consistency at a higher level an explicit description of the activi-ties needs to accompany any change in the calculation procedure
290
During the development of the model thoroughly checks have been made by all persons involved in the LULUCF section.
During the development of inventory thoroughly checks of the time-series have been made by all persons involved in the LULUCF section.
During the calculations the results are checked according to the check-list.
Output data to Data Storage Level 2 is checked for correctness accord-ing to the check-list.
All calculation principles are described in the NIR and the documenta-tion report (Gyldenkærne et al. 2005).
All theoretical reasoning is described in the NIR and the documenta-tion report (Gyldenkærne et al. 2005).
All theoretical reasoning is described in the NIR and the documenta-tion report (Gyldenkærne et al. 2005).
A clear reference in the DP level 1 to DS level 1 is under construction.
A manual log is under construction in the spread sheets.
Data Processing
level 1
5.Correctness DP.1.5.1 Shows at least once by independent calculation the correctness of every data manipulation
Data Processing
level 1
5.Correctness DP.1.5.2 Verification of calculation results using time-series
Data Processing
level 1
5.Correctness DP.1.5.3 Verification of calculation results using other measures
Data Processing
level 1
5.Correctness DP.1.5.4 Shows one to one correctness between external data sources and the data bases at Data Storage level 2
Data Processing
level 1
7.Transparency DP.1.7.1 The calculation principle and equations used must be described
Data Processing
level 1
7.Transparency DP.1.7.2 The theoretical reasoning for all meth-ods must be described
Data Processing
level 1
7.Transparency DP.1.7.3 Explicit listing of assumptions behind all methods
Data Processing
level 1
7.Transparency DP.1.7.4 Clear reference to data set at Data Storage level 1
Data Processing
level 1
7.Transparency DP.1.7.5 A manual log to collect information about recalculations
291
A manual check list is under development for correct connection be-tween all data types at level 1 and 2.
A manual check list is under development for correctness of data im-port to level 2.
!����������
Danish Energy Agency (2001). Denmark’s Greenhouse Gas Projections until 2012. Ministry of Environment and Energy, Danish Energy Agency. ISBN 87-7844-213-3. http://www.ens.dk/graphics/Publika-tioner/Klima_UK/ReportGHG5dk_3May2001.pdf
Danish Forest and Nature Agency (2000). Evaluering af den gennem-førte skovrejsning 1989–1998. Miljø- og Energiministeriet, Skov- og Naturstyrelsen, 2000. [Evaluation of afforestation areas 1989-1998. Ministry of Environment and Energy, National Forest and Nature Agency, 2000.] ISBN: 87-7279-241-8.
De danske Plantningsforeninger, 2004. Mr. Helge Knudsen, personal comm.
Dralle, K., Johannsen, V.K., Larsen, P.H. (2002). Skove og plantager 2000. Skoven 8: 339-344.
Gyldenkærne, S. Münier, B, Olesen, J.E., Olesen, S.E. Petersen, B.M. and B.T. Christensen, 2005 Opgørelse af CO2-emissioner fra arealan-vendelse og ændringer i arealanvendelse LULUCF (Land Use, Land Use Change and Forestry), Metodebeskrivelse samt opgørelse for 1990 – 2003, Arbejdsrapport fra DMU 213, 81 s. http://www.dmu.dk-/Udgivelser/Arbejdsrapporter/Nr.+200-249/
http://www.statistikbanken.dk/. Data on annually harvested round-wood in the period 1990-2002.
Johannsen, V.K. (2002) Dokumentation af beregninger i forbindelse med Skovtælling 2000. Skovstatistik, Arbejdsnotat nr. 6, Skov & Land-skab. 156 pp. [Documentation of calculations in Forestry Census 2000. Forest Statistics Working Paper No. 6, Forest & Landscape, Hørsholm, Denmark]
Larsen, P.H. and Johannsen, V.K. (2002) (eds.). Skove og Plantager 2000. [Forestry Census 2000]. Statistics Denmark, Skov & Landskab, Danish Forest and Nature Agency. ISBN 87-501-1287-2.
Moltesen, P. (1988). Skovtræernes ved. [The wood of forest trees]. Skovteknisk Institut, Akademiet for Tekniske Videnskaber. ISBN 87-87798-52-2.
Data Storage
level 2
5.Correctness DS.2.5.1 Documentation of a correct connection between all data type at level 2 to data at level 1
Data Storage
level 2
5.Correctness DS.2.5.2 Check if a correct data import to level 2 has been made
292
Møller, C.M. (1933). Bonitetsvise tilvækstoversigter for Bøg, Eg og Rødgran i Danmark. [Yield tables for different site classes of beech, oak and Norway spruce in Denmark]. Dansk Skovforenings Tidsskrift 18.
Nihlgård, B. and Lindgren, L. (1977). Plant biomass, primary produc-tion and bioelements of three mature beech forests in South Sweden. Oikos 28: 95-104.
PDIR 2004, Gødninger m.m. Fortegnelse over deklarationer, producen-ter og importører 2004. Available at: http://www.pdir.dk/Files-/Filer/Virksomheder/Goedning/Oversigt/Pjo/Fortegnelse_2004.doc
Petersen, B.M., Olesen, J.E. & Heidmann, T., 2002. A flexible tool for simulation of soil carbon turnover. Ecological Modelling 151, 1-14.
Petersen, B.M., 2003. C-TOOL version 1.1. A tool for simulation of soil carbon turnover. Description and users guide. http://www.-agrsci.dk/c-tool Danish Institute of Agricultural Sciences, Denmark. 39 pp. (C-TOOL can be downloaded from this site).
Petersen, B.M., Berntsen, J., Hansen, S. & Jensen, L.S., 2005. CN-SIM - a model for the turnover of soil organic matter. I. Long-term carbon and radiocarbon development. Soil Biology & Biochemistry 37, 359-374.
Schöne, D. and Schulte, A. (1999). Forstwirtschaft nach Kyoto: Ansätze zur Quantifizierung und betrieblichen Nutzung von Kohlenstoffsen-ken. Forstarchiv 70: 167-176.
Statistics Denmark (1994). Forests 1990. ISBN 87-501-0887-5.
Vande Walle I., Mussche, S., Samson, R., Lust, N. and Lemeur, R. (2001). The above- and belowground carbon pools of two mixed de-ciduous forest stands located in East-Flanders (Belgium). Ann. For. Sci. 58: 507-517.
Vesterdal, L., Ritter, E., and Gundersen, P. (2002). Change in soil or-ganic carbon following afforestation of former arable land. For. Ecol. Manage. 169: 137-143.
293
�$$����@�
��� Emission from organic soils. Literature data corrected to average Danish climate (Svend E. Olesen, Danish Institute of Agricul-tural Sciences). Crop Country Peat type Depth, m Distance to
water table, m climate corr.
factor Emission, C ha-1 y-1
Source Permanent grass Finland Fen/moor ? not drained -0,6-0,9 Tolonen & Turonen (1996)
Holland Fen/moor >1,0 0,3-0,4 0,7 0,5-1,0 Shothorst (1977)
Holland Fen/moor >1,0 ,0,55-0,6 0,7 1,2-2,1 Shothorst (1977)
Holland Fen/moor >1,0 0,7 0,7 2,4-2,8 Shothorst (1977)
The waste sector consists of the CRF source category 6.A Solid Waste Disposal on Land, 6.B. Wastewater Handling, 6.C. Waste Incineration and 6.D. Other.
For 6.A. Solid Waste Disposal on Land CH4 emissions are considered in the following as a result of calculations in continuation of previ-ously used and reported methodology. A slight change in the method-ology was made for this submission as compared to the 2006 submis-sion, following the advice from reviewers, refer to details below.
For 6.B. Wastewater Handling, the CH4 and N2O emissions are arrived at from a survey carried out 2004-2005 and were introduced in the in-ventory submissions for the first time in 2005, and in the NIR in 2005. For the submission in 2006 the methodology was somewhat revised. For this submission no change was made in methodology since the 2006 submission.
For the CRF source category 6.C. Waste Incineration, the emissions are included in the energy sector since all waste incinerated in Denmark is used in energy production.
For the source sector 6.D. “Other” emissions from combustion of bio-gas in biogas production plants are included (mentioned as Gasifica-tion of biogas in the CRF tables). These emissions have existed since 1994 and are small for all years from 1994 to 2005 – below 3.68 Gg CO2 equivalents (1999), taken as the sum of the GHG contributions. For all years this “Other” is dominated by CO2 contributing more than 99% to the category.
In Table 8.1, an overview of the emissions is presented. The emissions are taken from the CRF tables and are presented as rounded figures.
297
��������� Emissions (Gg CO2 equivalents) for the waste sector
� � �� ��� ��� ��� � � �� �� ��� ��� ��
6 A. Solid Waste Disposal on Land CH4 1 335 1 359 1 369 1 383 1 345 1 301 1 291 1 231 1 190 1 215
������������ ����������� ������� is the dominant source in the sector with contributions in the time-series varying from 75.7% (2002) to 87.6% (1992) of total Gg CO2 equivalents. In 2005, the contribution is 77.0%. Throughout the time-series, the emissions are decreasing due to a reduction in the amount of waste deposited.
������� ��� ����������� For this source,�CH4 contributes the most to the sectoral total, varying between contributions of 7.8% (1991) to 20.3% (2002). In 2005 the contribution is 18.4%. In absolute terms, the CH4 emission from this source displays a slightly overall increasing trend in the time-series resulting from the increase in industrial influ-ent load of total organic wastewater, a decrease in the final sludge dis-posal category “combustion” and the small recovery of methane po-tential by biogas production. N2O from this source contributes with between 3.8 to 5.8% of the sectoral total. In absolute terms, N2O emis-sions decrease over the time-series. The decrease is due to technical upgrading of wastewater treatment plants resulting in a decrease in ef-fluent wastewater loads, i.e. decrease in activity data, determining the indirect emission of N2O, which is the major contributor to the emis-sion of N2O.
As a result, the sectoral total in CO2 equivalents decreases throughout the time-series. Compared with 1990, the 2004 emission is 11.2% lower, Table 8.1.
For many years, only managed waste disposal sites have existed in Denmark. Unmanaged and illegal disposal of waste is considered to play a negligible role in the context of this category.
The CH4 emission from solid waste disposal on land at managed Solid Waste Disposal Sites (SWDS) constitutes, in 2005 as in previous years,
298
a key source category. However, previously it was a key source with regard to level and trend, in 2005 it is key with regard to level only. In the key-source level analysis for 2005, it is number 11 of 21 key sources and contributes with 1.6% of the national total. As regards the key-source trend analysis for 2005, the category is number 21 on the list, where 20 sources are keys (Cf. Annex 1). The emission estimates for the CH4 emission is decreasing with 20.7% from 1990 to 2005.
A quantitative overview of this source category is shown in Table 8.2 with the amounts of landfilled waste, the annual CH4 gross emissions from the waste, the CH4 collected at landfill sites and used for energy production, the amount of CH4 gas oxidised and the resulting emis-sions for the years 1990-2005. The amount of waste and the resulting CH4 emission can be found in the CRF tables submitted as well.
In general, the amount of deposited waste has decreased markedly throughout the time-series. This is a result of action plans by the Dan-ish government called the "Action plan for Waste and Recycling 1993-1997" and "Waste 21 1998-2004". The latter plan had, inter alia, the goal to recycle 64%, incinerate 24% and deposit 12% of all waste. The goal for deposited waste was met in 2000. Further, in 1996 a municipal ob-ligation to assign combustible waste to incineration was introduced. In 2002, the Danish Government set up new targets for the year 2008 for waste handling in a “Waste Strategy 2004-2008” report. According to this strategy, the target for 2008 is a maximum of 9% of the total waste to be deposited. In the waste statistics report for the year 2004, data shows that this target was met, since 8% of total waste was deposited in 2004 (Danish Environmental Protection Agency, 2006a). Further in 2005 the amount decreased compared to 2004 and was only 7% of the total waste amount (DEPA 2006b).
The decrease in the emission throughout the time-series is marked, but much less so than the decrease in the amount of waste deposited. This is due to the time involved in the processes generating the CH4, which is reflected in the model used for emission calculation.
299
��������� Waste amounts in landfills and their CH4 emissions 1990-2005.
Year Waste Annual Biogas Gas Annual
gross collected oxidized net
emission emission
kt kt CH4 kt CH4 kt CH4 kt CH4 kt CO2-eqv
1990 3 175 71.1 0.5 7.1 63.6 1 335
1991 3 032 72.6 0.7 7.2 64.7 1 359
1992 2 890 73.9 1.4 7.2 65.2 1 369
1993 2 747 74.9 1.7 7.3 65.9 1 383
1994 2 604 75.8 4.6 7.1 64.1 1 345
1995 1 957 76.3 7.4 6.9 62.0 1 301
1996 2 507 76.5 8.2 6.8 61.5 1 291
1997 2 083 76.3 11.1 6.5 58.6 1 231
1998 1 859 76.1 13.2 6.3 56.6 1 190
1999 1 467 75.7 11.5 6.4 57.8 1 215
2000 1 482 75.3 11.0 6.4 57.9 1 215
2001 1 300 74.0 10.0 6.4 57.6 1 209
2002 1 174 72.3 11.2 6.1 55.0 1 155
2003 966 70.2 7.9 6.2 56.1 1 178
2004 1 000 68.3 11.0 5.7 51.6 1 084
2005 957 66.7 10.7 5.6 50.4 1 059
Disposal of waste takes place at 134 registered sites (year 2001, DEPA 2006b). The organic part of the deposited waste at these sites generates CH4 gas, of which some is collected and used as biogas in energy-producing installations at 26 sites (2003).
��&��� '�����#�������!���
%�����$�����������(������������The data used for the amounts of municipal solid waste deposited at managed solid waste disposal sites is (according to the official registra-tion) worked out by the Danish Environmental Protection Agency (DEPA) in the so-called ISAG database (DEPA 1997, 1998, 2000, 2001a, 2001b, 2002, 2004a, 2004b, 2005, 2006a and 2006b). The registration of the amounts of waste deposited takes place in the ISAG database in the following waste categories:
• Domestic Waste • Bulky Waste • Garden Waste • Commercial & Office Waste • Industrial Waste • Building & Construction Waste • Sludge • Ash & Slag However, for CH4 emission estimates, a division of waste types is needed in categories with data for the Degradable Organic Carbon (DOC) content. For the following categories, investigations of DOC content etc. have been carried out for Danish conditions:
• Waste food • Cardboard
300
• Paper • Wet cardboard and paper • Plastics • Other combustible • Glass • Other, not combustible The Danish investigation shows that the waste types contain the frac-tion of DOC as shown in Table 8.3.
��������� Fraction of DOC in waste types.
Waste Type DOC-fraction of Waste
Waste food 0.20
Cardboard 0.40
Paper 0.40
Wet cardboard and paper 0.20
Plastics 0.85
Other combustible 0.20 - 0.57
Glass 0
Since the Danish solid waste disposal sites (SWDSs) are well-managed, it is assumed that a methane correction factor of 1 can be used (GPG page 5.9, Table 5.1). Furthermore, 0.50 is used as the fraction of DOC dissimilated, which is considered good practice (GPG page 5.9). Fi-nally, the fraction of CH4 in landfill gas is taken as 0.45 (GPG page 5.10). These parameters lead to the calculation of a “general emission factor” for DOC as shown in Table 8.4. In the model formulation in this table oxidisation (in sub layers) has been kept and now been put to 0 - as compared to previous submission 0.10 - following the advice from reviewers, further on this in the text below.
�������� Calculation of general emission factor for DOC.
Combining Table 8.3 and Table 8.4 give emission factors for waste types, Table 8.5.
Parameter Description Input Calculation ______________________________________________________________________________________________ a fraction of DOC oxidised in sub layers 0 ------------------------------------------------------------------------------------------------------------------------------------------------------------- 1 – a = b fraction of DOC not oxidised in sub layers 1 c fraction of DOC dissimilated 0.50 ------------------------------------------------------------------------------------------------------------------------------------------------------------- b • c fraction of DOC emitted as gas 0.50 ------------------------------------------------------------------------------------------------------------------------------------------------------------- d fraction of gas emitted as CH4 0.45 (as C) ------------------------------------------------------------------------------------------------------------------------------------------------------------- b • c • d fraction of DOC emitted as CH4 0.225 (as C) b • c • d • (12 + 4 • 1) /12 fraction of DOC emitted as CH4 0.30 (as CH4) =emf for DOC ______________________________________________________________________________________________ DOC: Degradable Organic Carbon
301
��������� CH4 emission factors according to waste types.
The emission estimates are built upon a composition of the deposited waste, as shown in Table 8.6, and are according to Danish investiga-tions.
��������� ISAG waste types and their content (fraction) of waste types with calculated emission factor.
Table 8.6 forms the connection between the ISAG data (left column) and waste type (upper row) where emission factors have been calcu-lated (Table 8.5). This composition is kept for the whole time-series.
The emission factors for the ISAG waste types are then calculated as the weighted average according to Table 8.5 and Table 8.6. The result is shown in Table 8.7.
��������� Emission factor (kg CH4/kg waste) for ISAG waste types.
Waste type DOC-fraction Fraction of waste of waste emitted as CH4 emf (1) (2) __________________________________________________________________________________________ Waste food 0.2 0.060 Card-board 0.4 0.120 Paper 0.4 0.120 Wet card-board and paper 0.2 0.060 Plastics 0.85 0.255 Other Combustible 0.20 - 0.57 0.060 - 0.171 Glass 0 0 Other not Combustible 0 0 __________________________________________________________________________________________ Column (2) is column (1) multiplied by emf for DOC ( = 0.30 ) “Other Combustible” varies in DOC-fraction according to ISAG waste types. Unit of column (2) is “fraction”. Example: 1 tonne of waste food: 60 kg of CH4 is emitted
302
The detailed explanation on the composition of waste and the method-ology to obtain emission factors in this section of the NIR report has also been given, since the parameters in the CRF format are found not to be fully descriptive for the Danish data and for the methodology used.
The review team on the 2005 submission of data and the 2005 NIR pointed out that a more correct way to model the process of CH4 emis-sion in a landfill, as regards the recovery and the oxidation factor, is to use the oxidation factor after recovery. Unfortunately, this interven-tion by the review team came in January 2006, too late to be used in the emission estimations for the CRF format for 2006 submissions. For the 2007 submission the change has been implemented, refer e.g. table 8.2 and text below.
)���(���������������!����The CH4 emission estimates from SWDSs are based on a First Order Decay (FOD) model suited to Danish conditions and according to an IPCC Tier 2 approach. The input parameters for the model are yearly amounts of waste, as reported to the ISAG database, and the emission factors according to Table 8.7. In the model, the half-life time of the carbon of 10 years is used, corresponding to:
k=ln2/10=0.0693 year-1 (refer GPG page 5.7)
which is in line with values mentioned in the GPG and close to the GPG default value of 0.05.
The time lag factor has been filled in the CRF-format as zero since the model used accounts for emissions from waste the same year as the waste is deposited.
The model calculations are not performed per landfill site, but for all waste deposited at all sites.
The yearly amounts of the different waste types and their emission factors are used to calculate the yearly potential emission. From the potential emission, the annual gross emission is calculated using the model. The CH4 captured by biogas installations at some of the sites is subtracted from this emission. The amounts of CH4 captured are ac-cording to the Danish energy statistics. Further, CH4 gas oxidised is subtracted. The result is annual net emissions. The waste amounts and the calculated CH4 emissions are shown in Table 8.8.
303
��������� Amounts of waste and CH4 emissions for 1990-2005
Year Domestic Bulky Garden Com- Industrial Building Sludge Ash & Waste Potential Annual Biogas Annual net Annual net Waste Waste Waste mercial Waste & cons- slag emission Gross collected emission emission & office struction Emission before after Waste Waste Total oxidation ox. 0.1
The total waste amount in Table 8.8 is the sum of the different waste types and thereby includes Industrial Waste, Building and Construction Waste. The total waste amount is reported as the activity data for the Annual Municipal Solid Waste (MSW) at SWDSs in the CRF Table 6.A. In so doing and in referring to the discussion of waste amounts in GPG, page 5.8, it is clear that these amounts are not really characteristics of the term “Municipal Solid Waste”. Furthermore, it should be noted that these amounts are used to calculate the amount of waste produced per capita in the Table 6A,C of the CRF and that these per capita amounts may not, therefore, be comparable with those used by other parties using different approaches.
The implied emission factor (IEF) in the CRF tables reflects an aggre-gated emission factor for the model. This IEF has increased through the time-series from 1990 to 2005, despite the general decreasing trend in the amount of waste. This is due to the model, where emissions from the waste deposited are being calculated to take place in years after the ac-tual year of deposition.
As mentioned in the section above, the review team pointed out that a more correct way to model the process of CH4 emission in a landfill, as regards the recovery and the oxidation factor, is to use the oxidation fac-tor after recovery. This suggestion is implemented in this submission.
This method corresponds to the assumption that the oxidation takes place in the top layers of the landfills. Since the Danish solid waste dis-posal sites (SWDSs) are well-managed, it is assumed that 10% of the CH4 produced by the waste is oxidised (OX = 0.1; refer GPG page 5.10). The consequence for the resulting emission is systematically increasing emis-sions. The increase is minor ranging from 0.08% (1990) to 2.38% (1998). For 2004 the increase is 2.03%.
Furthermore, an analysis has been carried out on the introduction of in-dividual half-life times for the emissions of CH4 from the waste sectors used, Table 8.9.
305
��������� Preliminary analyses of CH4 emissions for 1990-2005 using individual half-life time for waste sectors.
Comparing Table 8.9 with Table 8.8, it can be seen that the emissions us-ing individual half-life times are smaller for the whole time-series. The difference increases from 8% in 1990 to 20% in 2005. Please note that this comparison is for annual gross emission (not net emission). This ap-proach, including considerations with regard to the size of half-life times, will be analysed in more depth in the future based on references etc. in the IPCC, 2006.
In Annex 3.E, further details on the model for the CH4 emission from solid deposited waste are given.
������ ����� �� ������� ��� ������ ������
����� ����The parameters considered in the uncertainty analyses and the estimated uncertainties of the parameters are shown in Table 8.10. The reference is GPG, page 5.12, Table 5.2. For all uncertainties, symmetric values based on maximum numeric values are estimated as the uncertainties for the whole inventory is a Tier 1 approach to be summed up in the GPG Table 6.1. Uncertainties are estimated on parameters, which are mostly used in factors for multiplication, so that the final uncertainty is estimated with Equation 6.4 in the GPG.
As regards the uncertainty given in the GPG for the methane generation constant, k, (-40%, +300%), this uncertainty cannot be included in simple equations for total uncertainties, such as GPG Equations 6.3 and 6.4. The reason is that k is a parameter in the exponential function for the formula for emission estimates. The FOD model has, therefore, been run with the k-values representing those uncertainties (-40%: k=0.0416 (half-life time, 16 years), +300%: k=0.2079 (half-life time, 3.33 years) as compared to the k=0.069 (half-life time, 10 years) used in the present model. Based on
Domestic
Waste Bulky Waste
Garden Waste
Commercial & Office Waste
Industrial Waste
Building & construction
Waste Sludge Ash & Slag
Total Annual Gross CH4
emission KT
Half-life time
(year) 4 23 7 12 17 17 4
Year CH4 emission kt
1990 13.8 9.9 6.2 3.9 10.7 6.9 13.9 0.0 65.4
1991 14.0 10.4 6.0 4.3 11.1 6.9 13.2 0.0 65.9
1992 14.1 11.0 5.8 4.7 11.5 6.8 12.4 0.0 66.3
1993 14.3 11.5 5.4 5.1 11.8 6.7 11.5 0.0 66.4
1994 14.4 12.0 5.1 5.6 12.2 6.6 10.5 0.0 66.3
1995 14.4 12.5 4.7 5.9 12.4 6.4 9.6 0.0 66.0
1996 13.7 13.0 4.3 6.2 12.7 6.3 9.0 0.0 65.2
1997 12.5 13.4 3.9 6.7 12.9 6.1 8.6 0.0 64.2
1998 11.7 13.7 3.6 7.1 13.1 6.0 8.2 0.0 63.5
1999 11.2 14.1 3.3 7.5 13.2 5.8 7.9 0.0 63.0
2000 10.4 14.5 3.0 7.9 13.3 5.7 7.4 0.0 62.1
2001 9.4 14.6 2.8 8.1 13.3 5.5 6.7 0.0 60.4
2002 8.3 14.6 2.5 8.4 13.3 5.4 6.0 0.0 58.6
2003 7.3 14.7 2.3 8.5 13.1 5.2 5.5 0.0 56.6
2004 6.3 14.6 2.1 8.7 13.0 5.1 5.0 0.0 54.8
2005 5.4 14.7 1.9 9.0 12.9 4.9 4.4 0.0 53.3
306
these runs on potential emissions, mean differences on calculated CH4 emissions for 1990-2004 are found to be from –17.3% to +7.8%.
The final uncertainty on the emission factor is based on uncertainty esti-mates in Table 8.10a and, by means of the GPG Equation 6.4, is calcu-lated as:
Uncertainty of emission factor total % = SQRT(502+302+102+102+17.32) = 62.6%
��������� Uncertainties for main parameters of emissions of CH4 for SWDS
Parameter Uncertainty Note
The Waste amount sent to SWDS MSWT*MSWF
10% Since the amounts are based on weighing at the SWDS the lower value in GPG is used
Degradable Organic Carbon DOC 50%
Fraction of DOC dissimilated 30%
Methane Correction Factor 10%
Methane recovery and Oxidation Factor 10% see the text
Methane Generation Rate Constant 17.3% see the text
� ��� ������ ��������������������Registration of the amount of waste has been carried out since the begin-ning of the 1990s in order to measure the effects of action plans. The ac-tivity data is, therefore, considered to be consistently long enough to make the activity data input to the FOD model reliable. For further in-formation on activity data, refer to Annex E.
The consistency of the emissions and the emission factor is a result of the same methodology and the same model used for the whole time-series. The parameters in the FOD model are the same for the whole time-series. The use of a model of this type is recommended in the IPCC GL and GPG. The half-life time parameter used is within the intervals recom-mended by the IPCC GPG.
As regards completeness, the waste amounts used, as registered in the ISAG system, do not only include traditional Municipal Solid Waste (MSW), but also non-MSW as Industrial Waste, Building and Construc-tion Waste and Sludge. The composition of these waste types is, accord-ing to Danish data, used to estimate DOC values for the waste types (re-fer GPG page 5.10).
������ ����������� � ��� ���
������������The reviewers recommended an improved description and have in the review of the 2005 NIR acknowledged that this effort has taken place and has improved the NIR. It is the intention to publish a sector report for SWDS. The main effort has, however, centred on improving the descrip-tion in the NIR and this section is to be regarded as a further improve-ment.
A proposal for formal agreements with regard to data deliverance has been put forward to DEPA concerning provision of annual waste amounts. However, such an agreement has not yet been signed. Since it
307
is a statutory requirement that waste amounts are reported to DEPA, the agreement may potentially not be required (refer to the remarks under DS.1.3.1). DEPA makes a yearly report on the reception of the registra-tions, etc.
In general terms, for this part of the inventory, the Data Storage (DS) Level 1 and 2 and the Data Processing (DP) Level 1 can be described as follows:
��������������� �
The external data level refers to the placement of original data for amounts of waste categories or fractions. These categories/fractions are linked to data on waste types with known content of degradable organic carbon, see Section 8.2.2. Data for CH4 recovery are used. Further (exter-nal) data are parameters to the FOD model. For further details on the ex-ternal data, refer to the table below.
��������� Details on external data�
File or folder name Description AD or Emf. Reference Contact(s) Data agree-ment/ Comment
Report on 2005 amounts accord-ing to the waste fractions, Annex 1
Activity data Danish Envi-ronmental Protection Agency, Waste Statistics (Af-faldsstatistik)
Frank Marcher The amounts are registered due to statutory requirements
Basic Data (Grundda-ta05_kun tal.xls)
Dataset for energy-producing SWDS
CH4 recovery data
The Danish Energy Au-thority (DEA)
Peter Dal Prepared due to the obligation of DEA
swds_fod_model_2005.xls Excel file with the FOD model Parameters of the FOD model
IPCC GL GPG
Erik Lyck
�
����������� �������This level, for SWDS, comprises a stage where the external data are treated internally, preparing for the input to the NERI First Order of De-cay model, see Section 8.2.2. The model runs are carried out and the out-put stored.
���������� �������Data Storage Level 2 is the placement of selected output data from the FOD model as inventory data on SNAP levels in the Access (CollectER) database.
� ���������������The present stage of QA/QC for the Danish emission inventories for SWDS is described below for DS and DP level 1 Points of Measurement (PMs). This is to be seen in connection with the general QA/QC descrip-tion in Section 1.6 and, especially, 1.6.10 on specific description of PMs common to all sectors, general to QA/QC.
With regard to the general level of uncertainty, the amounts in waste fractions/categories are rather certain due to the statutory environment
Data Storage
level 1
1. Accuracy DS.1.1.1 General level of uncertainty for every dataset including the reasoning for the specific val-ues
308
for these data, while the distribution of waste fractions according to waste type and their content of DOC is more uncertain. It is generally ac-cepted that FOD models for CH4 emission estimates offer the best and the most certain way of estimation. The half-lifes in the FOD models are an important parameter with some uncertainty.
The uncertainties of the DEPA data are not available in the DEPA report-ing. The uncertainties are taken from the IPCC GL and GPG. A special uncertainty/sensitivity analyses connected to the uncertainty/variation of the half-life parameter is carried out. DEA data on CH4 recovery are considered to be precise. Refer to Section 8.2.3 on uncertainty.
Only some comparison of Danish data values from external data sources with corresponding data from other countries has been carried out in or-der to evaluate discrepancies. For many countries SWDS waste amounts do not – as for the Danish data – include waste from industrial sources, which presents a difficulty with regard to comparison.
The following external data sources are used for the inventory on SWDS (refer also to the table above):
• Danish Environmental Protection Agency, ISAG database: amounts of the various waste fractions deposited (refer to Section 8.2.2).
• A Danish investigation on the waste types in waste fractions and the content of degradable organic carbon in waste types.
• Danish Energy Authority: Official Danish energy statistics: CH4 re-covery data.
The selection of sources is obvious. The ISAG database is based on statu-tory registrations and reporting from all Danish waste treatment plants for all waste entering or leaving the plants. Information concerning waste in the previous year must be reported to the DEPA each year, no later than 31 January. Registration is made by weight. For recovery data, the DEA registers the energy produced from plants where installations recover CH4. for the energy statistics.
For the parameters of the FOD model, references are made to the IPCC GL and GPG.
Data Storage
level 1
1. Accuracy DS.1.1.2 Quantification of the uncertainty level of every single data value including the reason-ing for the specific values.
Data Storage
level 1
2.Comparability DS.1.2.1 Comparability of the data values with similar data from other countries, which are compa-rable with Denmark, and evaluation of dis-crepancy.
Data Storage
level 1
3.Completeness DS.1.3.1 Documentation showing that all possible national data sources are included by setting down the reasoning behind the selection of datasets.
Data Storage
level 1
4.Consistency DS.1.4.1 The origin of external data has to be pre-served whenever possible without explicit arguments (referring to other PMs).
309
The origin of external activity data has been preserved as much as possi-ble. The starting year for the FOD model used is 1960, using historic data for waste quantities. Since 1994, data is according to the Danish ISAG re-porting system. For further information on the origin of activity data, re-fer to Annex 3E. Files are saved for each year of reporting. In this way changes to previously received data is reflected and explanations are given.
The FOD model and its parameters have been used consistently, throughout the time-series, refer to Section 8.2.3.
It is a statutory requirement that amounts of waste are reported annually to DEPA, no later than January 31 for the previous year which corre-sponds well with the inventory development. No explicit agreement has yet been made.
The summary of the dataset can be seen in Table 8.8 in Section 8.2.2. For the reasoning behind the selection of the specific dataset, refer to DS 1.3.1.
These references exist in the description given in the Section 8.2.2.1, un-der methodological issues.
The following list shows the person responsible and contact information for delivery of data:
Tier 1 uncertainty calculations are made. The use of the Tier 1 methodol-ogy presumes a normal distribution of activity data and emission factor variability. The extent to which this requirement is fulfilled still needs to be elaborated. The uncertainty on the half-life time cannot be imple-mented on a Tier 1 level and a special assessment has been given, see DS.1.1.2.
Data Storage
level 1
6.Robustness DS.1.6.1 Explicit agreements between the external institution holding the data and NERI about the conditions of delivery.
Data Storage
level 1
7.Transparency DS.1.7.1 Summary of each dataset including the rea-soning for selecting the specific dataset
Data Storage
level 1
7.Transparency DS.1.7.3 References for citation for any external data-set have to be available for any single value in any dataset.
Data Storage
level 1
7.Transparency DS.1.7.4 Listing of external contacts for every dataset
Data Processing
level 1
1. Accuracy DP.1.1.1 Uncertainty assessment for every data source as input to Data Storage level 2 in relation to type of variability. (Distribution as: normal, log normal or other type of variability)
310
The uncertainty assessment has been given in Section 8.2.3. The uncer-tainty on the half-life time cannot be implemented on a Tier 1 level and a special assessment has been given, see DS.1.1.1.
An evaluation of the methodological approach, in comparison with the Tier 1 level, has been made, see Section 8.2.4. This shows that the emis-sions from waste estimated according to the default methodology from the IPCC GL and GPG will deviate considerably from those in this sub-mission, also since the waste amounts estimated in the latter methodolo-gies deviate from those used for Denmark.
From the evaluation carried out, see DP.1.1.3, it is clear that no direct verification can be carried out, since the method is a Tier 2 method, in accordance with the IPCC GL and GPG.
The calculation used is a Tier 2 methodology from the IPCC GL and GPG.
There is no quantitative knowledge on either (1) the shift over time in waste types within waste fractions and in DOC content in waste types or (2) possible individual conditions relating to the SWD sites.
There is no direct data to elucidate the points mentioned under DP.1.3.1.
There is no change in calculation procedure during the time-series and the activity data is, as far as possible, kept consistent for the calculation of the time-series.
Data Processing
level 1
1. Accuracy DP.1.1.2 Uncertainty assessment for every data source as input to Data Storage level 2 in relation to scale of variability (size of variation intervals)
Data Processing
level 1
1. Accuracy DP.1.1.3 Evaluation of the methodological approach using international guidelines
Data Processing
level 1
1. Accuracy DP.1.1.4 Verification of calculation results using guide-line values
Data Processing
level 1
2.Comparability DP.1.2.1 The inventory calculation has to follow the international guidelines suggested by the UNFCCC and IPCC.
Data Processing
level 1
3.Completeness DP.1.3.1 Assessment of the most important quantita-tive knowledge which is lacking.
Data Processing
level 1
3.Completeness DP.1.3.2 Assessment of the most important cases where access is lacking with regard to critical data sources that could improve quantitative knowledge.
Data Processing
level 1
4.Consistency DP.1.4.1 In order to keep consistency at a high level, an explicit description of the activities needs to accompany any change in the calculation procedure.
311
The model has been checked to give the results to be expected on fictive input data, se Annex 3E.
The time-series of activities and emissions in the FOD-model output, in the SNAP source categories and in the CRF format have been prepared. The time-series are examined and significant changes are checked and explained. Comparison is made with the previous year’s estimate and any major changes are verified.
The correct interpretation in the model of the methodology and the parameterisation has been checked, refer DP.1.5.1.
Data transfer control is made from the external data sources and to the SNAP source categories at level 2. This control is carried on further to the aggregated CRF source categories.
The calculation principle and equations are described in Section 8.2.2. Further transparency comes as a consequence of using TIER 2 method of the IPCC GL and GPG, described in these IPCC reports.
The theoretical reasoning is described in Section 8.2.2 and, due to the used of the Tier 2 method of the IPCC GL and GPG, is also described in these IPCC reports.
The assumption is that the emissions can be described according to a FOD model as described in the IPCC GL and GPG for SWDS. Further-more, it is assumed that this FOD model can be run with the parameters as they are listed in Section 8.2.2.
Data Processing
level 1
5.Correctness DP.1.5.1 Show at least once, by independent calcula-tion, the correctness of every data manipula-tion.
Data Processing
level 1
5.Correctness DP.1.5.2 Verification of calculation results using time-series
Data Processing
level 1
5.Correctness DP.1.5.3 Verification of calculation results using other measures
Data Processing
level 1
5.Correctness DP.1.5.4 Shows one-to-one correctness between external data sources and the databases at Data Storage level 2
Data Processing
level 1
7.Transparency DP.1.7.1 The calculation principle and equations used must be described
Data Processing
level 1
7.Transparency DP.1.7.2 The theoretical reasoning for all methods must be described
Data Processing
level 1
7.Transparency DP.1.7.3 Explicit listing of assumptions behind all methods
Data Processing
level 1
7.Transparency DP.1.7.4 Clear reference to dataset at Data Storage level 1
312
Refer to the table at the start of this Section (8.2.4).
Recalculation changes in the emission inventories are described in the NIR. The logging of the changes takes place in the yearly model file.
The full documentation for the correct connection exists through the yearly model file, its output and report files made by the CollectER data-base system.
This check is performed, comparing model output and report files made by the CollectER database system, refer to DS.2.5.1.
�� �����������������������The following points are a list of QA/QC tasks to be considered directly in relation to the SWDS part of the Danish emission inventories:
������������ �������
• A further comparison with external data from other countries in order to evaluate discrepancies.
• Agreement on the data deliverance consistency and stability. • Investigations into the possibility of obtaining data on variations in
waste fraction composition and DOC content in the time-series. ����������� �������More work on uncertainty calculations.
Further evaluation of FOD modelling with half-life time depending on individual waste types.
������������ �������� � ��� ���It is good practice, and a QA procedure, to compare the emission esti-mates included in the inventories with the IPCC default methodology.
In Table 8.11, default methodology is presented using the GPG and the IPCC GL, as appropriate. The parameters (on the pages of the IPCC GL and IPCC GPG) used are referred to in the table. As seen against the cal-culation of DOC in the default methodology, the Danish data is not suited for direct use. Referring to the formula in the GPG, p5.9, it is as-sumed (referring to Table 8.6, above) that A comprises “Cardboard”, “Paper” and “Wet Cardboard and Paper”; that B comprises “Plastic”, “Other Combustible” and “Other not Combustible”; and that C com-prises “Waste Food”. A mean fraction of these categories was calculated for use in the default methodology.
Data Processing
level 1
7.Transparency DP.1.7.5 A manual log to collect information about recalculations
Data Storage
level 2
5.Correctness DS.2.5.1 Documentation of a correct connection between all data types at level 2 to data at level 1
Data Storage
level 2
5.Correctness DS.2.5.2 Check if a correct data import to level 2 has been made
313
���������� IPCC default methodology for CH4 emissions from SWDS for 1990-2005�
The table shows that the default methodology underestimates the amounts of waste deposited and the CH4 emissions by a factor of 2-3. The reason for this is that the default methodology does not seem to in-clude Industrial Waste, which is deposited in considerable quantities in Denmark, Table 8.8.
A further option in the default methodology is to include the total waste amount registered with the waste generation rate for total waste, and in-clude the fraction of waste deposited to SWDS, Table 8.12. The fraction as well as the generation rate for total waste is included in the CRF Table 6 A “Additional Information”.
315
��������� As Table 8.11 but with ISAG registered waste amounts and fraction of waste deposited to SWDS.
The result of this adjusted default methodology is CH4 emissions, which in the beginning of the time-series represent highly overestimated emis-sions and in the later part of the time-series represent somewhat overes-timated emissions compared with the results of the FOD model. One ex-planation is that the FOD model reflects the ongoing process over the years with regard to the generation of CH4 from waste deposited in pre-vious years, while the default method only estimates emissions reflecting the waste deposited the same year.
������ ������ ����
For the submissions in 2007, recalculations have been carried out in rela-tion to the final submission in 2006 of inventories 1990-2004. The recalcu-lation represents the slight change in methodology as described above and updates in the energy statistics on the uptake of CH4 by installations at SWDSs for energy production for years 2003-2004. The recalculation implies for the whole time-series slight increasing CH4 emissions rang-ing from 0.08% in 1990 to 2.38% in 1998. For 2004, the recalculation is 0.93%.
������ ������ ������������
In response to the expert review team for the 2005 submissions, the methodology has been revised for this submission based on a new inter-pretation of the oxidation occurring in the landfills. Further analyses will be carried out on the impact and sensitivity of individual half-life times for different waste fractions. References in the 2006 IPCC Guidelines will be analysed in this context.
A further plan is to analyse the influence of a changed distribution of the composition of the deposited waste throughout the time-series. Data availability and expert judgement of the evidence for such an analysis are parts of the planned investigation.
Finally further QA/QC analyses will be taken into consideration.
�����������
(links as on the internet 3 April 2007)
Danish Environmental Protection Agency 2006b: Affaldsstatitik 2005 (Waste Statistics 2005). Orientering fra Miljøstyrelsen nr. 6. http://www2.mst.dk/common/Udgivramme/Frame.asp?pg=http://www2.mst.dk/Udgiv/publikationer/2006/87-7052-284-7/html/default.htm
IPCC 2006: 2006 IPCC Guidelines for National Greenhouse Gas Invento-ries, prepared by the National Greenhouse Gas Inventories Programme, Eggleston, H.S., Buendia, L., Miwa, K., Ngara, T & Tanabe, K. (eds). Pub-lished: IGES, Japan.
���� ������������ �������� ������������!�"#$�
����%� ������������!������ �� ���
This source category includes an estimation of the emission of CH4 and N2O from wastewater handling. CH4 is emitted from anaerobic treat-ment processes, while N2O may be emitted from anaerobic as well as aerobic processes. This source category does not include any key sources (cf. Annex 1).
318
The Danish Environmental Protection Agency (DEPA) publishes data from municipal and private wastewater treatment plants (WWTPs). The data includes an overview of the influent load of wastewater at Danish WWTPs, treatment categories, effluent quality parameters and final dis-posal categories and treatment processes for reuse of for sewage sludge. Information and data are reported at national level.
&��'����� �� ���The net emission of CH4 is calculated as the gross emission minus the amount of CH4 potential not emitted; i.e. recovered and flared or used for energy production. The not emitted methane potential is calculated as the amount of sludge used for biogas (and thus included in the CO2 emission from the energy production) or combusted (and thus included in the calculation of CO2 emission from the combustion processes). A summary of the calculated methane potentials of final disposal catego-ries constituting the summed up not emitted methane potential, the gross and resulting net emission of CH4 from 1990 to 2005 is given in Ta-ble 8.13.
��������� CH4 emissions recovered and flared or used for energy production, total methane potential not emitted, gross and net emission data [Gg].
Based on the figures, the percent methane potential not emitted as meth-ane due to combustion, i.e. ((CH4, external combustion + CH4, internal combustion) /
CH4 potential not emitted)) x 100%, has deceased from approximately 80% to 60% throughout the period, 1990-2005. A decrease in internal and exter-nal combustion is accompanied by an increase in combustion processes included in the production and reuse of sludge in sandblasting products from 14% to 54% of the total recovered methane potential.
( �������) ����� �� ���The emission of N2O from wastewater handling is calculated as the sum of contributions from wastewater treatment processes at the WWTPs and from sewage effluents. The emission from effluent wastewater, i.e. indi-
rect emissions, includes separate industrial discharges, rainwater-conditioned effluents, effluents from scattered houses, from mariculture and fishfarming. In Table 8.14, the contribution to the total emission of N2O from effluents is given in Columns 2 to 6. The total N2O emission from effluents is given in Column 7, the contribution from direct N2O emission in Column 8 and the total N2O emission, i.e. the sum of indirect and direct N2O emissions, is given in the last column.
��������� N2O emission from effluents from point sources, from wastewater treatment processes and in total [ton-nes].
* The individual contributions to the total N2O emission from effluent water has not been reported for 2004 and 2005. Numbers given for the year 2004 have been obtained by personal communication with the DEPA. Effluent reduction data does not support a continuing decrease in the N2O emission from effluent water. For this year, an overall aver-age from 1999-2002 was used assuming a constant level nitrogen load in the effluent water and a corresponding constant level of indirect N2O emission from WWTPs to prevail. However, if the indicated lower emission in 2003 and 2004 are continuing then the assumed constant level may wrong and the 2005 emission will be overestimated.
The direct emission trend increases slightly, reaching a stable level from 1997 onwards. The decrease in the indirect emission from wastewater ef-fluent is due to the technical upgrading of the WWTPs and the resulting decrease in wastewater effluent nitrogen loads. The indirect emission, which is the major contributor to the emission of nitrous oxide, is not ex-pected to decrease much more in future, as effluent reduction of N has increased from 65% in 1993 to 80% in 2004 (cf. Annex 3E, Table 3E.4).
����*� &��'����� �� ������
A country-specific methodology has been developed for estimating CH4 and N2O emissions for wastewater handling in Denmark as described in Thomsen and Lyck, 2005. This section is divided into methodological is-sues related to the CH4 and N2O emission calculations, respectively.
+�� � �!��������� �� ��������������� ���'����� �� ������������ �� ����The methodology developed for this submission for estimating emission of methane from wastewater handling follows the IPCC Guidelines (IPCC, 1997) and IPCC Good Practice Guidance (IPCC, 2000).
According to the IPCC GL, the emission should be calculated for domes-tic and industrial wastewater and the resulting two types of sludge, i.e. domestic and industrial sludge. However, the information available for the Danish wastewater treatment systems does not fit into the above categorisation as a significant fraction of the industrial wastewater is treated at centralised municipal wastewater treatment plants (WWTPs) and the data available for the total organic waste (TOW) does not differ-entiate between industrial and municipal sewage sludge.
���������������� �������������������������������� ��������From 1990 to 1998, the IPPC default methodology for household waste-water has been applied by accounting and correcting for the industrial influent load (cf. Annex 3.E, Table 3E.5 and 3E.5 and Figure 3E.1). TOW activity data used for calculating the gross emission are given in Table 8.15
���������� Total degradable organic waste (TOW) calculated by use of the default IPCC method corrected for contribution from industry to the influent TOW and country-specific data.
����� ����� ����� ����� ����� ����� ����� ����
Contribution from industrial inlet BOD 2.5 2.5 2.5 5.0 13.6 22.2 30.8 39.4
Population (1000) 5140 5153 5170 5188 5208 5228 5248 5268
Contribution from industrial inlet BOD 48 41 42 38 38 37 40.49 40.49 Population (1000) 5287 5305 5322 5338 5351 5384 5398 5410 TOW [Gg] - corrected IPPC method* 142.80 - - - - - - - TOW [Gg] - country-specific data - 140.25 141.49 144.36 156.18 160.21 153.06 153.04**
*TOW = (1+I/100) x (P x Ddom), where P is the Population number and Ddom= 18250 kg BOD/1000 persons/yr.
**Data for 2005 have not yet been released. Therefore, the estimated TOW data for 2005 are based on an average of two scenarios; scenario 1 is an estimate based on a linear regression of the country-specific TOW data and sce-nario 2 assumes that the country-specific data to represent a constant level of mean value 149.26 Gg.
The gross emission of CH4 is calculated by using the TOW data given in Table 8.15 multiplied by a country-specific emission factor (EF) derived as described in the next section.
��������� ��������� �������������������������������� ��������The emission factor (EF) is found by multiplying the maximum methane producing capacity (Bo) with the fraction of BOD that will ultimately de-grade anaerobic, i.e. the methane conversion factor (MCF). The default value for Bo, given in the IPCC (2002) of 0.6 kg CH4/kg BOD is used.
The fraction of sludge (in dry weight (dw) or wet weight (ww)) treated anaerobic is used as an estimate of the “fraction of BOD that will ulti-mately degrade anaerobically”. This fraction is set equal to MCF. By do-ing so it is assumed that all of the sludge treated anaerobic is treated 100% anaerobic and no weighted MCF is calculated. The per cent sludge that is treated anaerobic, aerobic and by additional different stabilisation methods are given in Table 8.16.
321
���������� Stabilisation of sludge by different methods in tonnes dry weight (dw) and wet weight (ww), respectively DEPA 1989, 1999, 2001and 2003 a.
*EF=Bo*MCF, where MCF equals the per cent amount of sludge treated anaerobic divided by 100 and Bo 0.6 kg CH4/kg BOD
**The report series “private and municipal wastewater” including data from 2003 to 2005 has not yet been released.
For comparison both the emissions factors based on wet weight and dry weight are given in Table 8.16 in the last column. The emission factor calculated from the dry weight fractions is fairly constant from year 1997 to 2002. It seems reasonable to assume a constant emission factor of 0.26 kg CH4 / kg BOD based on the dry weight fraction of sludge treated an-aerobic and an emission factor of 0.15 kg CH4 / kg BOD based on the wet weight fraction of sludge treated anaerobic. The emission factor based on wet weight is used for calculating the gross CH4 emission since it seems the most appropriate to use when combined with BOD data in the emission calculation procedure.
The uncertainty in the fraction of wastewater treated anaerobic is judged by the average and spread of average of data given above. Both anaero-bic fraction data based on wet and dry weight are included. The uncer-tainty is estimated to be 28%.
���������������� ��������������������������������������������Of the total influent load of organic wastewater, the separated sludge has different final disposal categories. The fractions that are used for biogas, combustion or reuse including combustion include methane potentials that are either recovered or emitted as CO2. The estimated methane po-
Biological Chemical
Year** Units Anaerobic Aerobic Other Total
EF (IPCC 1996)
[kg CH4 / kg BOD]*
1987 52401 24364 48760 125525
1997 65368 66086 19705 151159
1999 65268 70854 19499 155621
2000 68047 69178 21677 158902
2001 70992 68386 18638 158016
2002
Sludge amount in Tons dw
63500 58450 18071 140021
1987 41.7 19.4 38.9 100 0.25
1995 32 41 27 100 0.19
1996 32.7 41 26.3 100 0.20
1997 43.2 43.7 13.1 100 0.26
1999 41.9 45.5 12.5 100 0.25
2000 42.8 43.5 13.7 100 0.26
2001 45 43.3 11.7 100 0.27
2002
sludge amount in % of total dw
45 42 13 100 0.27
1997 363055 648686 149028 1160769
1999 336654 829349 271949 1437952
2000 459600 1110746 321427 1891773
2001 494655 1217135 330229 2042019
2002
Sludge amount in Tons ww
262855 827703 279911 1370469
1997 31.3 55.9 12.8 100 0.19
1999 23.4 57.7 18.9 100 0.14
2000 24.3 58.7 17.0 100 0.15
2001 24.2 59.6 16.2 100 0.15
2002
sludge amount in % of total ww
19.2 60.4 20.4 100 0.12
322
tentials of these fractions have been subtracted from the calculated (theo-retical) gross emission of CH4 as given in the summary Table 8.13.
Therefore, to arrive at the net emission of methane from the Danish WWTPs, the recovered, flared or otherwise not emitted methane poten-tial needs to be quantified. Available activity data for calculating the re-covered and flared CH4 potential (theoretical negative methane emis-sion) is given in Table 8.17.�
��������� Sludge in percent of the total amount of sludge and tonnes dry weights (dw) according to disposal categories of relevance to CH4 recovery.
Unit Year** Combustion internal
Combustion external
Biogas Other*
1987 24.6 18.5
1997 15.5 6.2 1.5 0.8
1999 7.4 14.8 1.9 9.1
2000 15.0 9.2 1.6 14.4
2001 14.8 6.3 1.0 11.3
Percent
2002 11.4 4.4 0.9 10.0
1987 23330 11665 7667
1997 23500 9340 2338 1211
1999 23008 9845 2972 14140
2000 11734 23591� ����� 22856
2001 23653 14532 1588 17883
Total tonnes dw
2002 15932 6120 1262 13989
*The category “Other” represents sludge which is combusted in cement furnaces and is used in further combusting processes for the production of sandblasting products.
**The Danish EPA has not yet released Data for 2003 to 2005.�
��������� ��������� ���������������������������������������������The IPCC GPG background paper (2003) estimates the maximum meth-ane producing capacity to be 200 kg CH4/tonnes raw dry solids, which is also the emission factor (EF), as the methane conversion factor (MCF) is equal to unity for biogas process (EF= Bo * MCF). The fraction of the gross CH4 emission, not emitted in reality, is then the dry weight of the biogas category multiplied by an EF of 200 kg CH4/tonnes raw dry sol-ids. The same EF is used for calculating the theoretical methane potential not emitted by the remaining disposal categories (cf. Thomsen & Lyck, 2005).
��������������������Based on the available data, simple linear regression of the methane po-tentials of the four disposal categories, given in Table 8.17, has been per-formed. These regression estimates together with the country-specific calculations forms the basis for the results given in Table 8.13. Details re-garding the results are addressed in Annex 3, Table 3E.7.
Time trends of the gross emission, the methane potential not emitted and the resulting net emission of methane, i.e. the last three columns of Table 8.13, is shown in Figure 8.1.
���������� Estimated time trends for the gross emission of methane (open squares), methane potential not emitted; i.e. sum of columns 2 to 5, or column 6, in Table 8.13 (crosses) and net emission of methane (open triangles).
The three grey regression lines represent approximations of the calcu-lated gross and net methane emissions and not emitted methane poten-tial for the time period 1990 to 2005. Figure 8.1 shows that the net emis-sion of methane on average increases 0.64 Gg per year, as a result of the increase in the gross emission of, on average, 0.76 Gg per year, and a mi-nor increase in the amount of methane potential not emitted by 0.12 Gg per year. The increasing trend in the net emission is a result of the indus-trial influent load of TOW, which has increased from an average of 2.5% in 1990 to an average contribution of 39.4% in the years from 1999 to 2004.
It should be mentioned that varying amounts of inflowing rainwater, as well as outflowing water, may contribute to “noise” or fluctuation in the time trend of the TOW used for calculating the gross emission of meth-ane 1999 to 2004. Time trends of the not emitted methane potential are difficult to interpret due to temporal changes in the individual final dis-posal categories contributing to the not emitted methane potential (cf. section 1.4.2 and 1.4.4).
Methodological issues related to the estimation of N2O emissions While CH4 is only produced under anaerobic conditions, N2O may be generated by nitrification (aerobic processes) and denitrification (an-aerobic processes) during biological treatment. Starting material in the influent may be urea, ammonia and proteins, which are converted to ni-trate by nitrification. Denitrification is an anaerobic biological conversion of nitrate into dinitrogen. N2O is an intermediate of both processes. Dan-ish investigation indicates that N2O is formed during aeration steps in the sludge treatments process as well as during anaerobic treatments; the former contributing most to the N2O emissions during sludge treatment (Gejlsberg et al., 1999).
324
����������� ������������������������������������A methodology for estimating the direct emission of N2O from wastewa-ter treatment processes has been derived (cf. Thomsen & Lyck, 2005).
The direct emission from wastewater treatment processes is calculated according to the equation:
������������������� � ���������� ����� ,,,, 22⋅⋅=
where �����is the Danish population, ���������� is the fraction of the Danish population connected to the municipal sewer system (0.9) and ������� ������ are the emission factors given in Table 8.17.
������������������ ������������������������������������The EF is derived from a factor of 3.2 g N2O/capita per year (Czepiel, 1995) multiplied by a correction factor of 3.52 to account for the indus-trial influent load. The correction factor of 3.52 is derived from the differ-ence in average nitrogen influent load at large and medium-sized WWTPs, divided by the influent load at large-size WWTPs (cf. Annex 3.E, Table 3.E.8). This approach is based on the assumption that the large-size WWTPs receive industrial wastewater while the medium size operators mainly receive wastewater from households (cf. Annex 3.E, and Thomsen and Lyck, 2005).
Until better data is available, simple regression of the relation between industrial influent load in percent and the EF is used for the years 1990 to 1997, after which the industrial contribution to the influent load is as-sumed constant and the EF of 10.8 g N2O/capita per year is used in the calculations. The influent load of nitrogen is assumed to increase in a similar way to the industrial influent loads of BOD given in percent in Table 8.18. The estimated Danish emission factors, as a function of the increase in industrial influent load in the Danish WWTPs, are given in Table 8.18.
���������� EF and activity data used for calculating the direct emission of N2O from wastewater treatment processes at Danish WWTPs.
The industrial loads of wastewater influent loads given in Table 8.18 for years 1990-2003 have been estimated from the original and registered data (Table 3.E.3, Annex 3.E). For the years 1990 to 1992, the industrial influent load is set to an average of 2.5%. From the years 1993 to 1997, the percentages are assumed to increase linear as shown in Table 8.18. The Danish emission factors are based on a regression of percent indus-trial loads versus the corrected emission factors given in Table 3.E.8 in Annex 3.E. The average fraction of industrial nitrogen influent is consid-ered constant from the year 1999 and forward. This is consistent with a fairly constant fraction of industrial wastewater influent from 1999 and forward.
����������������������������������� ����������� ������The IPCC default methodology only includes N2O emissions from hu-man sewage based on annual per capita protein intake. The methodol-ogy only accounts for nitrogen intake, i.e. faeces and urine. Not included are industrial nitrogen input and non-consumption protein from kitchen, bath and laundry discharges. The default methodology used for the 10% of the Danish population that is not connected to the municipal sewage system, is multiplied by a factor 1.75 to account for the fraction of non-consumption nitrogen (Sheehle and Doorn, 1997). For the remaining 90% of the Danish population, national activity data on nitrogen in discharge wastewater is available. This data is used in combination with the de-fault methodology for the 10% of the Danish population not connected to the municipal sewer system. 10% is added to the effluent N load to ac-count for the WWTPs not included in the statistics (DEPA 1994, 1996, 1997, 1998, 1999, 2001, 2002 and 2003). The formula used for calculating the emission from effluent WWTP discharges is:
( ) ( )( )[ ]�
��
��������������������� �������������� �
�����������
⋅⋅⋅⋅++⋅⋅⋅⋅=2
1.0 2
22 ,,,,,,
where � is the annual protein per capita consumption per person per year.
�� is the fraction of nitrogen in protein. i.e. 0.16 (IPCC (1997) GL, p 6.28)
�����is the Danish population
��� is the fraction of the Danish population not connected to the munici-pal sewer system, i.e. 0.1
� is the fraction of non-consumption protein in domestic wastewater. i.e. 1.75 (Sheehle and Doorn, 1997)
����� is the effluent discharged sewage nitrogen load (with 10% added to account for data not included in the statistics)
������� ��������� is the IPCC default emission factor of 0.01 kg N2O-N/kg sewage-N produced (IPCC (1997) GL, p 6.28)
����and ��� are the mass ratio i.e. 44/28 to convert the discharged units in mass of total N to emissions in mass N2O
���������������� ������������� ����������������������������In Table 8.19, activity data refers to the effluent discharged sewage nitro-gen load (DN, WWTP,).
326
���������� Discharges* of nitrogen from point sources [tonnes].
*The Danish EPA has not yet released Data for 2005.
�������������������The trends in the direct N2O emission from WWTPs, the indirect emis-sion from wastewater effluent and the total, as summarised in Table 8.14, are presented graphically in Figure 8.2.
��������������������������������� ������������ �
0
50
100
150
200
250
300
350
1990 1995 2000 2005����
������������������
���������� Time trends for direct emission of N2O (open squares), indirect emission, i.e. from wastewater effluents (open triangles) and total N2O emission (black triangles).
As explained in relation to the summary Table 8.14 in section 8.4.1, the decrease in the emission from effluent wastewater is due to the technical upgrade and centralisation of the Danish WWTPs following the adoption of the Action Plan on the Aquatic Environment in 1987. The increase in the direct N2O emission are following the increase in influent TOW by an increased connection of industry to the municipal sewage system; reach-ing a constant level from 1997-1999 and onwards.
It should be mentioned that varying amounts of inflowing rainwater, as well as outflowing water, may contribute to the “noise” or fluctuation in the time trend of the calculated indirect N2O emission (cf. section 1.4.2 and 1.4.4).
������ ,����� �� ������� ��-��� ������� �����!�
,����� ��!�The parameters considered in the uncertainty analyses and the estimated uncertainties of the parameters are shown in Table 8.20. For all uncer-tainties, symmetric values based on maximum numeric value are esti-mated.
Mariculture and fish farming 1737 1684 17351543 1494 1241 1418 2714 1757 1487 1162 1335
Municipal and private WWTPs 16884 15111 13071 10787 10241 89386387 4851 5162 5135 4653 4221 4528 3614 4027
327
���������� Uncertainties for main parameters of emissions for wastewater handling.
At this point, data regarding industrial on-site wastewater treatment processes is not available at a level that allows for calculation of the on-site industrial contribution to CH4 or N2O emissions. The degree to which industry is covered by the estimated emission is, therefore, de-pendent on the amount of industrial wastewater connected to the mu-nicipal sewer system. Any emissions from pre-treatment on-site are not covered at this stage of the method development.
The overall uncertainty on the emissions from uncertainty estimates in Table 8.21, and with the use of GPG Equation 6.3 and 6.4, is as follows:
������ �Uncertainty in estimating the gross emission of CH4, Ugross:
Ugross = SQRT(282+202+302) = 46.7%
Uncertainty in estimating the recovered or not emitted CH4, Unot emitted is estimated to be equal for all four categories at this stage:
Unot emitted = SQRT(302+502) = 58.3%
The total uncertainty, Utotal, associated with CH4 emission estimates is es-timated to be around 40%, using Equation 6.3 (IPCC (2000) page. 6.12) and uncertainty quantities (xi in eq. 6.3, IPCC (2000) set equal to the
Parameter Uncertainty Reference / Note Emission type
TOW ±20% Default IPCC value (GPG, Table 5.3, p 5.19); maximum uncertainty in the country-specific data is 28%
Maximum methane producing Capacity (Bo) ±30% Default IPCC value (GPG, Table 5.3, p 5.19)
Fraction treated anaerobically, i.e. the methane conversion factor (MCF)
±28% Based on the variation in registered data given in Annex 3.E , Table 3E.7
Gross CH4 emission
Methane potential ±50% Estimated based on IPCC background paper (2003)
Final disposal category data ±30% Estimated to be equal to the uncertainty in influent loads of organic matter
Not emitted CH4
EFN2O,direct ±30%
Calculated from average and standard deviation on data from Table 8.13, the uncertainty is around 10%. Due to uncertainty in the industrial influent load I, (cf. Annex 3.E, eq.1), the uncertainty at this point is set to 30%
Fconnected ±5% Set equal to uncertainty on population number Npop is the Danish population number ±5% Default from IPCC GPG
Direct N2O emission
P is the annual protein per capita consumption per person per year �±30% Not known / NERI estimate
FN is the fraction of nitrogen in protein 0% Empirical number without uncertainty
Npop is the Danish population number ±5% Default from IPCC GPG
Fnc is the fraction of the Danish population not connected to the municipal sewer system
±5% Set equal to uncertainty on population
F is the fraction of non-consumption protein in domestic wastewater
±30% Not known / NERI estimate
DN.WWTP is the effluent discharged sewage nitrogen load ±30% Not known / NERI estimate
EFN2O.WWTP.effluent is the IPCC default emission factor of 0.01 kg N2O-N/kg sewage-N produced
±30% Not known / NERI estimate
MN2O 0% Empirical number without uncertainty
Indirect N2O emission
328
yearly average fraction treated anaerobically or by final sludge catego-ries leading to a reduction in the gross emission.
��������!��� �Uncertainty estimates for the direct N2O emission, Udirect:
Udirect = SQRT(302+52+52) = 30.8%
Uncertainty in the indirect N2O emission, Uindirect, has been calculated as the uncertainty in the emission from the population connected and not connected to a WWTP, respectively, by use of Eq. 6.3 in the IPCC (2000) GPG.
The uncertainty associated with the emission of N2O based on the pro-portion of the population not connected to a WWTP:
Unot connected = SQRT(302+52+52+302+302) = 52.4%
The uncertainty in the emission from wastewater based on the propor-tion of the population connected to a WWTP:
Uconnected = SQRT(302+302) = 42.4%
The resulting total uncertainty in the N2O emission is estimated to be in the region of 26% at this stage. The total uncertainty has been estimated based on uncertainty quantities equal to the fraction of the population connected and not connected to a WWTP, respectively. These fractions were multiplied by the average effluent N from households and WWTPs including industrial wastewater treatment, respectively (cf. Annex 3E, Table 3E.11 and Thomsen & Lyck, 2005). When the uncertainty quantities are set equal to the fraction connected and not connected, the total uncer-tainty estimate is 25% (Eq. 6.3, IPCC GPG).
. ��-��� ������� �����!����������������The frequency and form of registration of the different activity data, which are critical for the calculation of the emission of methane as well as nitrous oxide, is of varying quality.
Registration of the activity data needed for the calculation of nitrous ox-ide emission from the effluent water has been registered as a measure of the effectiveness (distance to target) of the Action Plan on the Aquatic Environment in 1987. However, especially data on final disposal catego-ries used for calculating the amount of recovered, e.g. not emitted meth-ane are limited. Until now data has been extracted from different report series published by DEPA and from Statistics Denmark. DEPA consis-tency and completeness are expected to be improved by a harmonised databases published by DEPA. Existing data collection extracted from different reports will be verified against DEPA internet accessible data-bases (http://www.mst.dk/Vand/Spildevand/).
������ /+0/�������� � �� ���
/+0/�-����������The emission estimates methodology for wastewater handling was in-troduced for the first time in the inventory submission in March-April
329
2005. Data in this methodology has been updated and revised for the current submission. A description of the final methodology used will be published with reference to the methodological development and verifi-cation, by means of comparing country-specific methods with default methodologies, as described in Thomsen & Lyck, 2005.
In general terms, for this part of the inventory, the Data Storage (DS) Level 1 and 2 and the Data Processing (DP) Level 1 can be described as follows:
1�� ������2����%�The external data level refers to the placement of input data used for de-riving yearly activity and emission factors; references in terms of report and databases used for deriving input for the emission calculations. Re-ports and a list of links to external data sources are stored in a common data storage system including all sectors of the yearly NIRs.
���������� Overview of yearly stored external data sources at DS level1
*The data storage level 1 consists of DEPA reports and other sources listed in the Table.
"��#����������$�����%�This level, for wastewater handling, comprises a stage where the external data are treated internally, preparing for the input to the country-specific models, see Section 8.4.1 and 8.4.2. Programming as to automatically cal-culations based on activity data and emission factors are not yet fully operational. Calculations are carried out and the output stored in a not editable format each year. The DP at level 1 are expected to be re-designed to fit into a more uniform and easily accessible data reporting format regarding the derivation of activity data and emission factors used in the model calculations and included in the NIR submission 2008.
"��&������$�����'�Data Storage Level 2 is the placement of selected output data from the country-specific models as inventory data on SNAP levels in the Access (CollectER) database.
File or folder name Description AD or EF mf. Reference Contact(s) Data agree-ment/ Comment
Report series may be found a:t
www.mst.dk or stored at NERI data-exchange folder I:\ROSPROJ\LUFT-_EMI\Inventory\waste sector\ 6 B. Wastewater Handling\NIR2007\DS1\
Yearly/ Each second year reporting fre-quency.
Activity data Report series from DEPA: “�����������
����� � ��������
�� ����������������
���������������” and “� ���������”.
Karin Dahlgren Laursen
none
http://faostat.fao.org Annual protein con-sumption
Activity data FAOSTAT Marianne Thomsen
none
http://www.statistikbanken.dk/FU5 Population Activity data Statistics Denmark Marianne Thomsen
none
http://danva.dk Medium and small WWTP influent data used for calculating a correction factor accounting for the industrial contribution to wastewater charac-teristics
Emission factor
The Danish water and wastewater institution
Marianne Thomsen
none
330
�� ������������������The present stage of QA/QC for the Danish emission inventories for wastewater handling is described below for DS and DP level 1 Points of Measurement (PMs). This is to be seen in connection with the general QA/QC description in Section 1.6 and, especially, 1.6.10 on specific de-scription of PMs common to all sectors, general to QA/QC.
With regard to the general level of uncertainty, the amounts in final dis-posal categories and i.e. the amount of not emitted methane are rather uncertain due to the missing systematic registering and definitions of the final disposal categories. In addition the activity data for calculating the direct and indirect nitrous oxide emission are scattered between different sources of varying reporting frequency and format of reporting. Im-provements in terms of data agreements have been initiated.
Quantitative uncertainty measures of country-specific and measured data are not available. The uncertainties are either calculated or defaults numbers are taken from the IPCC GL and GPG and presented in Section 8.4.3.
Comparison of Danish data values with data sources from other coun-tries has been carried out in order to evaluate discrepancies as presented in the national verification report by Fauser et al., 2006 and the method-ology report by Thomsen & Lyck, 2005).
Methodology, reasoning and relevance of data sources used as input at DS level 1 are planned to be discussed and improved in cooperation with the DEPA. Subjects to be discussed are: the possibility and relevance of including direct nitrous emissions from separate industries; sub-models, e.g. for calculating in influence on rainwater in the influent wastewater and resulting effluent amount of nitrogen as this is causing fluctuations in the yearly indirect emissions of nitrous oxide; completeness of input data.
The origin of external activity data has been preserved as much as possi-ble. Files are saved for each year of reporting in a non editable format. In this way changes to previously received data and calculations is reflected and explanations are given.
Data Storage
level 1
1. Accuracy DS.1.1.1 General level of uncertainty for every dataset including the reasoning for the specific values
Data Storage
level 1
1. Accuracy DS.1.1.2 Quantification of the uncertainty level of every gle data value including the reasoning for the ecific values.
Data Storage
level 1
2.Comparability DS.1.2.1 Comparability of the data values with similar data from other countries, which are comparable with Denmark, and evaluation of discrepancy.
Data Storage
level 1
3.Completeness DS.1.3.1 Documentation showing that all possible national data sources are included by setting down the reasoning behind the selection of datasets.
Data Storage
level 1
4.Consistency DS.1.4.1 The origin of external data has to be preserved whenever possible without explicit arguments (referring to other PMs).
331
This point is especially critical due to the missing timing of DEPA report-ing and submission date of the yearly NIR. Clarification regarding possi-ble optimisation of data and delivery agreement are planned.
A summary of the data set can be seen in section 8.4.1 and 8.4.2. For the reasoning behind the selection of the specific dataset, refer to methodol-ogy report by Thomsen & Lyck, 2005.
These references exist in the description given in the Section 8.3, under methodological issues. In addition, they are directly accessible from the reports given in the list of references including link to internet accessible formats and stored every year in the given data exchange folder at NERI (cf. Table 8.21).
Will be clarified.
Tier 1 uncertainty calculations are made. The use of the Tier 1 methodol-ogy presumes a normal distribution of activity data and emission factor variability. Uncertainties are reported in Table 8.21.
The uncertainty assessment has been given in Section 8.4.2 and 8.4.3.
An evaluation of the methodological approach, in comparison with the check and default IPCC methodology l, has been performed and is pre-sented in Annex 3 and Thomsen & Lyck, 2005.
Data Storage
level 1
6.Robustness DS.1.6.1 Explicit agreements between the external institu-tion holding the data and NERI about the condi-tions of delivery.
Data Storage
level 1
7.Transparency DS.1.7.1 Summary of each dataset including the reason-ing for selecting the specific dataset
Data Storage
level 1
7.Transparency DS.1.7.3 References for citation for any external dataset have to be available for any single value in any dataset.
Data Storage
level 1
7.Transparency DS.1.7.4 Listing of external contacts for every dataset
Data Processing
level 1
1. Accuracy DP.1.1.1 Uncertainty assessment for every data source as input to Data Storage level 2 in relation to type of variability. (Distribution as: normal, log normal or other type of variability)
Data Processing
level 1
1. Accuracy DP.1.1.2 Uncertainty assessment for every data source as input to Data Storage level 2 in relation to scale of variability (size of variation intervals)
Data Processing
level 1
1. Accuracy DP.1.1.3 Evaluation of the methodological approach using international guidelines
332
This has been performed in Thomsen & Lyck, 20056 and in the NIR 2006 submission.
The calculations follow the IPCC GL and GPG.
There is no quantitative knowledge on the characteristics of industrial versus domestic influent organic carbon. Furthermore cf. DP 1.1.2 re-garding accuracy. Uniform definitions of final disposal categories are needed.
To be assessed once a systematic reporting format replacing the former report series from the DEPA are in place. Information on methane emis-sions for separate industries may be of importance. In addition changes in final disposal categories and related methane potentials recovered or not emitted.
There have been small changes in the calculation procedure during the time-series due to small changed in the data gap filling procedure with respect to TOW activity data. As far as possible, the calculation proce-dures are kept consistent for the calculation of the time-series.
The model has been checked by comparison with the IPCC default methodologies as presented in Thomsen & Lyck, 2005.
The time-series of activities and emissions in the model output, in the SNAP source categories and in the CRF format have been prepared. The time-series are examined and significant changes are checked and ex-plained.
Data Processing
level 1
1. Accuracy DP.1.1.4 Verification of calculation results using guide-line values
Data Processing
level 1
2.Comparability DP.1.2.1 The inventory calculation has to follow the international guidelines suggested by the UNFCCC and IPCC.
Data Processing
level 1
3.Completeness DP.1.3.1 Assessment of the most important quantitative knowledge which is lacking.
Data Processing
level 1
3.Completeness DP.1.3.2 Assessment of the most important cases where access is lacking with regard to critical data sources that could improve quantitative knowledge.
Data Processing
level 1
4.Consistency DP.1.4.1 In order to keep consistency at a high level, an explicit description of the activities needs to accompany any change in the calculation procedure.
Data Processing
level 1
5.Correctness DP.1.5.1 Show at least once, by independent calcula-tion, the correctness of every data manipula-tion.
Data Processing
level 1
5.Correctness DP.1.5.2 Verification of calculation results using time-series
333
The correct interpretation in the model of the methodology and the parameterisation has been checked as far as possible, refer DP.1.5.1.
Data transfer control is made from the external data sources and to the SNAP source categories at level 2. This control is carried on further to the aggregated CRF source categories.
The calculation principle and equations are described in Section 8.4.3. Further transparency becomes realised by further implementation of the NERI QA/QC plan as described in chapter 1.6.
The theoretical reasoning is described in Section 8.4.3 and in Thomsen & Lyck, 2005.
The assumption is that the emissions can be described according to the applied methodology and models as these are developed in accordance to the IPCC GL and GPG for wastewater handling.
Refer to the Table 8.22 and DS.1.1.1 above.
Recalculation changes in the emission inventories are described in the NIR. The logging of the changes takes place in the yearly model file.
The full documentation for the correct connection exists through the yearly model file, its output and report files made by the CollectER data-base system.
Data Processing
level 1
5.Correctness DP.1.5.3 Verification of calculation results using other measures
Data Processing
level 1
5.Correctness DP.1.5.4 Shows one-to-one correctness between external data sources and the databases at Data Storage level 2
Data Processing
level 1
7.Transparency DP.1.7.1 The calculation principle and equations used must be described
Data Processing
level 1
7.Transparency DP.1.7.2 The theoretical reasoning for all methods must be described
Data Processing
level 1
7.Transparency DP.1.7.3 Explicit listing of assumptions behind all methods
Data Processing
level 1
7.Transparency DP.1.7.4 Clear reference to dataset at Data Storage level 1
Data Processing
level 1
7.Transparency DP.1.7.5 A manual log to collect information about recalculations
Data Storage
level 2
5.Correctness DS.2.5.1 Documentation of a correct connection be-tween all data types at level 2 to data at level 1
Data Storage
level 2
5.Correctness DS.2.5.2 Check if a correct data import to level 2 has been made
334
This check is performed, comparing model output and report files made by the CollectER database system, refer to DS.2.5.1.
������ ������ ����
The emissions from wastewater handling were until the 2005 submission reported as zero. So, the methodology used for the CRF Source Category 6B for CH4 and N2O emissions is included for the third time in this sub-mission. Smaller revisions as compared to the 2006 submission have been performed. However, these revisions do not change previously re-ported emissions.
����"� ������ ������������
&��������(�)(*����� ��������������������As described in chapter 8.4.4.��
In addition, the suggestions in the review report on the 2005 submission, wherein the expert review team encouraged Denmark to estimate the domestic and the industrial wastewater contributions separately. This suggestion has been considered. National Statistics reports total TOC for industrial and household wastewater only. Separate emission estimated for domestic and industrial wastewater could be achieved for the pur-poses of comparison, by simply dividing the total TOW influent load ac-cording to percent contributions from industry and household, respec-tively.
Furthermore, the expert review team encouraged Denmark to make revi-sions to the reporting of N2O emissions from human sewage and waste-water effluent. For 2008 submissions, the N2O emissions from human sewage will be reported in 6.B.3 and the remaining emission from wastewater treatment will be reported in Domestic and Commercial wastewater, as suggested.
�����������
Danish Environmental Protection Agency 1994: Point Sources 1993. In Danish: Punktkilder 1993, Orientering fra Miljøstyrelsen, nr, 8. http://www.mst.dk/
Danish Environmental Protection Agency 1996: Point Sources 1995. In Danish: Punktkilder 1995, Orientering fra Miljøstyrelsen, nr, 16. http://www.mst.dk/
Danish Environmental Protection Agency 1997: Point Sources 1996. In Danish: Punktkilder 1996, Orientering fra Miljøstyrelsen, nr, 9. http://www.mst.dk/
Danish Environmental Protection Agency 1998: Point Sources 1997. In Danish: Punktkilder 1997, Orientering fra Miljøstyrelsen, nr, 6. http://www.mst.dk/
335
Danish Environmental Protection Agency 1999: Point Sources 1998. In Danish: Punktkilder 1998, Orientering fra Miljøstyrelsen, nr, 6. http://www.mst.dk/
Danish Environmental Protection Agency 2001: Point Sources 2000. In Danish: Punktkilder 2000, Orientering fra Miljøstyrelsen, nr, 13. http://www.mst.dk/
Danish Environmental Protection Agency 2002: Point Sources 2001. In Danish: Punktkilder 2001, Orientering fra Miljøstyrelsen, nr, 7. http://www.mst.dk/
Danish Environmental Protection Agency 2003: Point Sources 2002. In Danish: Punktkilder 2002, Orientering fra Miljøstyrelsen, nr, 10. http://www.mst.dk/
Danish Environmental Protection Agency 2004: Point Sources 2003. In Danish: Punktkilder 2003, Orientering fra Miljøstyrelsen, nr, 16. http://www.mst.dk/
Danish Environmental Protection Agency 2005: Point Sources 2003. Re-vision. In Danish: Punktkilder 2003 – revideret, Orientering fra Miljøsty-relsen, nr, 1. http://www.mst.dk/
Danish Environmental Protection Agency 1989: Wastewater from mu-nicipal and private wastewater treatment plants in 1987. In Danish: Spil-devandsslam fra kommunale og private renseanlæg i 1987, Orientering fra Miljøstyrelsen, nr. 10. http://www.mst.dk/
Danish Environmental Protection Agency 1999: Wastewater from mu-nicipal and private wastewater treatment plants in 1997. In Danish: Spil-devandsslam fra kommunale og private renseanlæg i 1997, Miljøprojekt, nr. 473. http://www.mst.dk/
Danish Environmental Protection Agency 2001: Wastewater from mu-nicipal and private wastewater treatment plants in 1999. In Danish: Spil-devandsslam fra kommunale og private renseanlæg i 1999, Orientering fra Miljøstyrelsen, nr, 3, 2001. http://www.mst.dk/
Danish Environmental Protection Agency 2002: Nonylphenol and non-ylphenolethoxylater in wastewater and sludge. In Danish: Miljøprojekt Nr, 704 (2002), Nonylphenol og nonylphenolethoxylater i spildevand og slam, Bodil Mose Pedersen og Søren Bøwadt, DHI - Institut for Vand og Miljø, Miljøstyrelsen, Miljøministeriet.
Danish Environmental Protection Agency 2003: Wastewater from mu-nicipal and private wastewater treatment plants in 2001. In Danish: Spil-devandsslam fra kommunale og private renseanlæg i 2000 og 2001, Ori-entering fra Miljøstyrelsen, nr, 9, 2003. http://www.mst.dk/
Danish Environmental Protection Agency 2004: Wastewater from mu-nicipal and private wastewater treatment plants in 2002. In Danish: Spil-devandsslam fra kommunale og private renseanlæg i 2002, Orientering fra Miljøstyrelsen, nr, 5, 2004. http://www.mst.dk/
FAOSTAT data, 2004: Food Supply http://apps.fao.org/faostat/collec-tions?version=ext&hasbulk=0 "last updated August 2004"
Thomsen, M. and Lyck, E. 2005: Emission of CH4 and N2O from waste-water treatment plants (6B). NERI Research Note No. 208.
Braun, R. and Wellinger, A. Potential of Co-digeation, 2003. IEA Bio-energy, Task 37 http://www.novaenergie.ch/iea-bioenergy-task37 /Dokumente/Potential%20of%20Codigestion%20short%20Brosch221203.pdf
Czepiel, P., Crill, P. & Harriss, R. 1995: Nitrous oxide emissions from municipal wastewater treatment, Environmental Science and Technol-ogy, 29, pp, 2352-2356.
Gejlsbjerg, B., Frette, L., Westermann, P. 1999: N2O release from active sludge, water and soil. In Danish: Lattergasfrigivelse fra aktiv-slam,Vand & Jord, 1,pp,33-37.
Schön, M., Walz, R., Angerer, G., Bätcher, K., Reichert, J., Bingemer, H., Heinemeyer, O., Kaiser, E.-A., Lobert, J., Scharffe, D. (1993). Emissionen der Treibhausgase Distickstoffoxid und Methan in Deutschland. In Forschungsbericht 104 02 682. UBA FB 93 121. Umweltbundesamt. Erich Schmidt Verlag, Berlin, publisher. (In German.) http://www.esv.in-fo/id/350303495/katalog.html
Scheehle, E.A. & Doorn, M.R.J. 1997: Improvements to the US, Wastewa-ter Methane and Nitrous Oxide Emission Estimates, US EPA.
DANVA 2001: Operating characteristics and key data for waste water treatment plants. In Danish: Driftsforhold og nøgletal for Renseanlæg 2000, http://www,danva,dk/sw220.asp
IPCC background paper 2003: CH4 and N2O emissions from waste water handling. http://www.ipcc-nggip.iges.or.jp/public/gp/bgp/5_2_CH-4_N2O_Waste_Water.pdf
���� �����3�� ���� �������� ������������!�"�$�
����%� ������������!������ �� ���
For the CRF source category ���� �����������������the emissions are in-cluded in the energy sector since all waste incinerated in Denmark is used in energy production.
The amounts of waste incinerated are given in the CRF-Table 6A,C.
337
As regards further information on waste incineration, see the Energy sec-tor in this report.
��"� �����4�'�������� ������������!�"1$�
��"�%� ������������!������ �� ���
Emission from the combustion of biogas in biogas production plants is included in CRF sector 6D. The fuel consumption rate of the biogas pro-duction plants refers to the Danish energy statistics. The applied emis-sion factors are the same as for biogas boilers (see NIR Chapter 3, En-ergy).
338
5� 4�'���������������6$�
In CRF Sector 7, there are no activities and emissions for the inventories of Denmark. For the inventories of the Kingdom of Denmark (Denmark, Faroe Islands and Greenland) emissions for Faroe Islands and Greenland are in Sector 7.
See Annex 6.1 and 6.2.
339
%7� ������ ������� ������������
The CRF recalculation tables for Denmark produced with the new CRF software do not include the recalculations for Denmark made since the NIR submission in April 2006. The reason for this is that selection of the CRF submission against which the recalculations are to be seen has been carried out by the UNFCCC Secretariat and cannot be changed by the parties. At present, the CRF includes as the submission to which the re-calculations relate the 2006 submission for the Kingdom of Denmark (i.e. Denmark as well as Greenland and the Faroe Islands), which has been submitted parallel to the inventories for Denmark. However, only one recalculation database can be included per party. We have communi-cated this problem to the CRF helpdesk. Further, we have noticed some errors in data in the columns “previous submission” in the recalculation tables (Tables 8(a)), e.g. values for SF6 in CRF 2003, where the values in “previous submitted” cell never was submitted and no recalcultion oc-cured. Therefore, this chapter is based on an excel file made with links to actually in 2006 submitted values. The analysis made is for Denmark only (excluding Greenland and Faroe Island). An extraction of the file only showing source categories for which there have been recalculations is Table 10.1. The aggregation level of the analyses is the level also used in the CRF recalculation tables.
%7�%� 8)��� �������9��� � �� �������������� ����
Explanations and justifications for the recalculations performed for this submission and since submission of data in the CRF-format for submis-sion to UNFCCC due April 15, 2006 for Denmark are given in the follow-ing sector chapters:
8����!:�
• Stationary Combustion Chapter 3.2.5 • Transport Chapter 3.3.5 • Fugitive emissions Chapter 3.5.5 3������!� � No recalculation
����������4�'�����������,�� Chapter 5.2.5
+�� ������ Chapter 6.8
2,2,��� Chapter 7.10
�����• Solid Waste Disposal on Land Chapter 8.2.5 • Wastewater No Recalculation. The main improvements are:
340
8����!�
&��������*��+������For stationary combustion plants the emission estimates have been up-dated according to latest energy statistics published by the Danish En-ergy Authority. The update includes the years 1990-2004. This is the main reason for the changes in this sector. However changed fuel type aggregation also caused imperceptible changes.
The distribution of emissions from the industrial sector, 1A2 was up-dated based on new information from Statistics Denmark & Danish En-ergy Authority. The total emission from category 1A2 was not affected only the distribution between the sub-sectors 1A2a-1A2f. However 1A2 is affected in recalculations due to the updated energy statistics.
Harmonisation of the GHG inventory and the information compiled for the ETS is on-going.
��+�����������The biggest changes for CO2 are for agriculture, where updated stock in-formation for tractors and harvesters 2001-2004, has given a fuel use and emissions increase for these years. A corresponding emission amount is subtracted from stationary sources, due to the overall national energy balance.
Minor changes are:
1) The residual fuel use amount from the fishery sector in the national energy statistics has been moved to the national sea transport category, resulting also in emission changes 1990-2004.
2) Some diesel oil fuel use has been subtracted from the fishery sector, in order to correct an error in last year’s submission for 1990-2004.
In total, the CO2 emission changes for agriculture/forestry/fisheries are between -1 and +3% from 1990 to 2004.
The biggest change in the CH4 emissions is calculated for road traffic, due to the use of new emission factors from COPERT IV, new mileage data from the Danish vehicle inspection programme, and a small fuel use correction. The emission changes are between +12% (1990) and -11% (1998). The military sector uses derived emission factors from road transport, and consequently emission changes are also calculated for military, except for CO2 and SO2. The emission changes are, however, small.
Only very small changes are calculated for N2O.
For NOx, CO and NMVOC the biggest emission changes occur for road transport, due to changes in emission factors and mileage data. For the same emission components, and for SO2, emission changes also occur for navigation and fisheries, caused by emission factor changes and fuel use reallocations (as explained for CO2).
341
3������!�No methodological changes have been introduced in the 2005 GHG in-ventory. Harmonisation of the GHG inventory and the information compiled for the ETS is on-going.
������A survey based on new methodologies results in new NMVOC emission estimates. Revisions have been made regarding use of pentane and sty-rene in the plastic industry, use and emission factors of glycolethers, use and emission factor of tertrachloroethylene and reassignment of some product groups from degreasing to paints.
+�� �������Small changes for emissions from the agricultural sector have taken place. These changes reflect increased emissions from years 1990-2004 by less than 1 %. There is no change in the calculation methodology. Based on the expert review team request, the feed consumption for dairy cattle 1990 – 1994 has been interpolated, in order to remove the time-series in-consistency. Another change is due to updated norm data for nitrogen excretion in 2003 and new data for export of living poultry from 1994.
�����The methodology for CH4 emissions from solid waste disposal sites has been slightly changed following a suggestion by the review team. The point was in the decay model to change the use of the oxidation factor, so that the subtraction of CH4 due to oxidation was done after the sub-traction due to recovered CH4. The change has resulted in an increase in yearly CH4 emission from solid waste disposal sites (SWDS) for the time series up to maximum of 2.4 %.
2,2,����
*������,��������������������A small recalculation has been made for the area converted from crop-land and grassland to wetlands. The total area affected by this is >400 hectares y-1 or less than 0.02 % of the Danish agricultural area. The influ-ence on the emission estimate is almost zero.
%7�*� 3�� �� ���������� �� ��������
For the National Total CO2 Equivalent Emissions without Land-Use, Land-Use Change and Forestry, the general impact of the improvements and recalculations performed is small and the changes for the whole time-series are between -0.02 % and +0.18 %. Therefore, the implications of the recalculations on the level and on the trend, 1990-2004, of this na-tional total are small, refer Table 10.1.
For the National Total CO2 Equivalent Emissions with Land-Use, Land-Use Change and Forestry,� the general impact of the recalculations is rather small, although the impact is larger than without LULUCF due to recalculations in the LULUCF sector for 2003 and 2004. The differences vary between –1.01 % and +0.14 %. These differences refer to recalcu-lated estimates, with major changes in the LULUCF for those years, refer Table 10.1.
342
���������� Recalculation performed year 2007 for 1990-2004. Differences in pct of CO2-eqv between this and the April 2006 submission for DK (Excluding Greenland and Faroe Islands)
It is a high general priority in the considerations leading to recalculations back to 1990 to have and preserve the consistency of the activity data and emissions time-series. As a consequence, activity data, emission factors and methodologies are carefully chosen to represent the emissions for the time-series correctly. Often, considerations regarding the consistency of the time-series have led to recalculations for single years when activity data and/or emission factors have been changed or corrected. Further-more, when new sources are considered, activity data and emissions are introduced to the inventories for the whole time-series, based as far as possible on the same methodology.
The implication of the recalculations is further shown in Tables 10.2-10.4.
No review has taken place on the submission 2006, since this review has be postponed to be performed in connection to the review of the initial report under the Kyoto Protocol. So the most recent review was a cen-tralized review on the 2005 submission.
The status for the review to come is that Denmark has received by 15 De-cember 2006 the Syntheses and Assessment Report part I and responded to that report by 5 January 2007. Further Denmark received by 27 Febru-ary 2007 the Syntheses and Assessment Report part II and responded to that report by 20 March 2007.
The suggestions and views of the expert review team on the 2005 sub-mission in their report dated 24 February 2006 has been studied and im-plemented as far as possible. A suggestion was to make a change in the methodology for CH4-emissions from solid waste disposal. This change has now been made; refer to Chapter 8.2.5.
Total CO2 Equiv. Emissions with Land-Use Change and Forestry -0,01 0,04 0,08 0,14 0,03 -0,02 0,03 0,03 0,04 0,05
Total CO2 Equiv. Emissions without Land-Use Change and Forestry -0,01 0,04 0,08 0,14 0,03 -0,02 0,03 0,03 0,04 0,05
��� ���� ����� ����� ����� ���� ����
Total CO2 Equiv. Emissions with Land-Use Change and Forestry 0,05 0,06 0,01 -0,53 -1,01
Total CO2 Equiv. Emissions without Land-Use Change and Forestry 0,05 0,06 0,01 0,13 0,18
343
Table 10.2 Recalculation for CO2 performed year 2007 for 1990-2004. Differences in CO2-eqv between this and the April 2006 submission for DK (Exclud-ing Greenland and Faroe Islands)
Table 10.3 Recalculation for CH4 performed year 2007 for 1990-2004. Differences in CO2-eqv between this and the April 2006 submission for DK (Exclud-ing Greenland and Faroe Islands)
Table 10.4 Recalculation for N2O performed year 2007 for 1990-2004. Differences in CO2-eqv between this and the April 2006 submission for DK (Exclud-ing Greenland and Faroe Islands)
Annex 2 Detailed discussion of methodology and data for estimating CO2 emission from fossil fuel combustion
Annex 3 Other detailed methodological descriptions for individual source or sink categories (where relevant)
3A Stationary combustion plants
3B Transport
3C Industry - no annexes to industry for this NIR
3D Agriculture
3E Waste
3F Solvents
Annex 4 CO2 reference approach and comparison with sectoral approach, and relevant information on the national energy balance
Annex 5 Assessment of completeness and (potential) sources and sinks of greenhouse gas emissions and removals excluded
Annex 6.1 Additional information to be considered as part of the NIR submission (where relevant) or other useful reference information
Annex 6.2 Additional information to be considered as part of the NIR submission (where relevant) or other useful reference information - Greenland/Faroe Islands
Annex 7 Tables 6.1 and 6.2 of the IPCC good practice guidance
Annex 8 Other annexes – (Any other relevant information)
Annex 9 Annual emission inventories 1990-2005 CRF tables for Denmark
The key source analysis is carried out according to the IPCC Good Prac-tice Guidance (GPG). The base year in the analysis is the year 1990 for the greenhouse gases CO2, CH4, N2O and 1995 for the greenhouse F-gases HFC, PFC and SF6. The base year is not adjusted for electricity im-port/export. The analysis was made for the inventory for the year 2005.
The present key source analysis follows the same approach as the analy-ses for the years 2000, 2001, 2002, 2003 and 2004 as presented in NIR 2002, 2003, 2004, 2005 and 2006 respectively. The approach is a Tier 1 quantitative analysis. As suggested in the Good Practice Guidance, the analysis is carried out without considering LULUCF.
The level assessment of the key source analysis is a ranking of the source categories in accordance to their relative contribution to the national total of greenhouse gases calculated in CO2-equivalent units. The level key sources are found from the list of source categories ranked according to their contribution in descending order. Level key sources are those from the top of the list and of which the sum constitutes 95% of the national total.
The trend assessment of the key source analysis is a ranking of the sour-ce categories according to their contribution to the trend of the national total of greenhouse gases, calculated in CO2-equivalents, from the base year to the year under consideration. The trend of the source category is calculated relative to that of the national totals and the trend is then weighted with the contribution, according to the level assessment. The ranking is in descending order. As for the level assessment, the cut-off point for the sum of contribution to the trend is 95% and the source cate-gories from the top of the list to the cut-off line are trend key sources.
# ����$������!���""��"������
The starting point for the choice of source categories is presented in the GPG as Table 7.1. This table constitutes a suggested list of source catego-ries for the key source analysis. It is mentioned in the GPG that catego-ries for the key source analysis should be chosen in a way so that emis-sions from a single category are estimated with the same method and the same emission factor. Therefore, for categories in Table 7.1, which in our Corinair database are composed of activities with different emission fac-tors or estimated with different methods, splits were made accordingly. It is in the energy sector, with its major emission contributions, that fur-ther splits are made as compared to Table 7.1 in the Good Practice Guid-ance.
347
The source categories for energy and stationary combustion are defined according to the fuels and their emission factors, which for year 2005 are as follows
CO2 emission factors, fossil kg/GJ
COAL 95
COKE OVEN COKE 108
PETROLEUM COKE 92
PLASTIC WASTE 17.6
RESIDUAL OIL 78
GAS OIL 74
KEROSENE 72
ORIMULSION 80
NATURAL GAS 56.96
LPG 65
REFINERY GAS 56.9
For Energy and stationary, combustion categories in the key source ana-lyses are composed according to the fuels mentioned. The split made in the analyses for year 2003 between brown coal and coke-oven coke is, in this analysis, not of importance since brown coal, according to the En-ergy Statistics, is not used in 2005.
For energy and mobile combustion, the basis for the source categories is the activities:
Category for KS anal CRF Cat
part of CRF Cat CRF cat descr.
1 Mobile combus-tion
Civil aviation 1.A.3.a
Transport
2 Mobile combus-tion
Road transportation 1.A.3.b
Transport
3 Mobile combus-tion
Railways 1.A.3.c
Transport
4 Mobile combus-tion
Navigation 1.A.3.d
Transport
5 Mobile combus-tion
Military 1.A.5.b
Other Mobil
6 Mobile combus-tion
National fishing 1.A.4.c
Other Sectors Agr/Fores/Fisheries
7 Mobile combus-tion
Agriculture 1.A.4.c
Other Sectors Agr/Fores/Fisheries
8 Mobile combus-tion
Forestry 1.A.4.c
Other Sectors Agr/Fores/Fisheries
9 Mobile combus-tion
Other mobile and machin-ery/industry 1.A.2.f
Manif Industries and C Other
10 Mobile combus-tion
Household and gardening 1.A.4.b
Other Sectors. b. Residential
The categories above, numbered 1 - 5, are directly found in the CRF-tables, while numbers 6 – 8 are found under CRF category 1.A.4.c., num-ber 9 under 1.A.2.f and number 10 under 1.A.4.b. These categories have been chosen as source categories for the analysis due to differences in the use of fuels and fuel types and resulting differences in emission factors.
For the sectors Industry, Agriculture and Waste, the source categories in the key source analyses are activities found in the CRF source categorisa-tion.
348
The selection of key source categorisation made for the key source analy-sis is well argued in relation to the intentions of the analysis in the GPG and the decision to keep the selection has been made in order not to lose the ability to make comparisons with the key source analysis performed for the years 2000, 2001, 2002, 2003 and 2004. Our choice of categories for the analysis identifies 72 source categories, which appear in the table sec-tion of this Annex in Table 3. The key source categories are listed accord-ing to the inventory section in which they appear. As compared to the analysis made for year 2004 (refer NIR 2006) with 71 categories, 1 addi-tional category has been identified which is CH4 from gasification of bio-gas.
The entries in the results of the key source analyses in Table 1 and Table 2 for the years 1990 and 2005 are composed from the databases produc-ing the CRF inventory for those years in this report. Note that base-year estimates are not used in the level assessment analysis, but are only in-cluded in Table 1 to make it uniform with Table 2. The analyses are car-ried out on the basis of the new CRF used since the april 2006 submis-sions.
The result of the key source level assessment for Denmark for 2005 is shown in Table 1. 21 key sources were identified and marked as shaded in the table. In 2004, 2003 and 2002, the number of key sources was also 21, in 2001 and 2000 the number was 20.
The result of the key source trend assessment for Denmark for 2005 is shown in Table 2. A number of 20 key sources (21 in 2004 and 2003, 17 in 2002 and 2001, and 16 in 2000) were identified and marked as shaded in the table. Note that according to the GPG, the analysis implies that con-tributions to the trend are all calculated as mathematically positive to be able to perform the ranking.
Following the reporting suggestion of the GPG, the key source analysis is summarised in Table 3. The information in this table is given in an order to allow ��������������������� ��������� (Table 7) in the new CRF for-mat. In Table 3, all categories used in the analysis are listed and the summary result of the key source analyses is given. It is seen that of the 72 source categories chosen for this analysis, 24 are identified as key source categories either in the level or in the trend analysis or in both. In 2004 this number was 24 out of 71 source categories. In 2003, this number was 25 out of 67 categories and in 2002 25 out of 63 categories. In 2001 and 2000 out of 59 categories 23 and 22 were key sources, respectively. In the key source analysis for 2005, 16 key sources were key in both level and trend. This number was 18 in 2004 and 2003. In 2002 this number was 15, and 14 in both 2001 and 2000. In 2005, five sources were key sources for level only (four in 2004, three in 2004, seven in 2002 and six in 2001 and in 2000). In 2005, four sources were key in trend only as in 2004 and 2003 (three in 2002, three in 2001 and two in 2000).
The ��������������������������������������������������� ����� con-tribute with six key source categories in 2005 with respect to level and
349
trend (also six in 2004 and 2003, seven in 2002, seven in 2001 and five in 2000). These six key sources are, as in 2004 and 2003, the major fuels Coal, Petroleum Coke, Plastic Waste, Residual Oil, Gas Oil and Natural gas. For these key sources the trend in emission estimates, comparing 1990 and 2005, Coal, Residual Oil and Gas Oil are seen to decrease, while Plastic Waste and Petroleum Coke and especially Natural Gas increase. According to the key source level assessment Coal is the most contribut-ing category in 2005 with 22.8% of the national total (Table 1). Also in 2004, 2003, 2002 and 2001, Coal was the most contributing category, in 2004 contributing 25.5%. This contribution was at a maximum in 2003 where it was 30.5%, compared with 2002 where it was 24.4% and where it had increased from 24.0% in 2001 and 23.0% in 2000. Natural gas is in 2005 as in 2004, 2003, 2002 and 2001, the third largest contributor with 16.9% (16.4% in 2004, 15.1% in 2003, 16.6% in 2002, 16.0% in 2001 and 15.5% in 2000). Gas Oil is, in 2005 the seventh largest contributor with 3,8% (in 2004 4.0%, in 2003 3.9%, in 2002 4.3%, in 2001 4.4% and in 2000 4.2%). The rest of the categories mentioned in this paragraph as level and trend key sources each contribute below 2.6% of the national total in 2005. Refinery gas is, as in 2004, a key source according to level only and contributes, in 2005, with 1.4% with a slight increasing emission estimate from 1990 to 2005.
The ������� ������� ���� ���� ��������� ������������ ���� ����� con-tributes with the category Road Transportation as a key source for level and trend with increasing emission estimates from year 1990 to 2005. This category is in year 2005, as in 2004, 2003, 2002, 2001 and in 2000, the second largest contributor to the national total among the categories in this analysis, with a level contribution of 19.0% in 2005 as compared to 17.7% in 2004, 16.0% in 2003, 16.6% in 2002, 16.2% in 2001 and 16.4% in 2000. The source CO2 from Mobile Combustion Agriculture is in 2005, as in 2004 and 2003, a key source with respect to both level and trend. For this source the trend in emission estimates from 1990 to 2005 is falling and the contribution to the national total in 2005 is 1.7%. The source CO2 from Mobile Combustion National Fishing is in 2005, as in 2003, a key source with respect to both level and trend. In 2004 this category was a key source with respect to trend only. The emission estimates from 1990 to 2005 are falling and the contribution is down to 0.7% in 2005. The source CO2 from Mobile Combustion Navigation is a key source accord-ing to level as it was in 2004 and 2003, in 2005 contributing 0.8% and with slightly decreasing emission estimates from 1990 to 2005. The source CO2 from Mobile Combustion, Other Mobile and Machinery is a key source according to level and trend with a contribution of 1.5% in 2004 and a slight increase in emission estimates from 1990 to 2005.
The source category CO2 from Fugitive Emissions Oil and Natural Gas is in the 2005 analysis a key for trend only while in the 2004 analysis it was key for both trend and level; this source was key for trend only in 2003. The contribution in 2005 is 0.7% and the emission estimates from 1990 to 2005 are increasing.
The source category CH4 as Non-CO2 Emission from Stationary Combus-tion is in the 2005 analysis, a key source for level and trend, as in 2004. The contribution in 2005 is 0.8% and the emission estimates from 1990 to 2005 increase markedly.
350
In the���� �������������, two sources are keys with respect to both level and trend in 2005. In 2004, 2003 and 2002 this number was three. The two keys in 2005 are CO2 emissions from Cement Production and emission from Substitutes for Ozone Depleting Substances (HFCs and PFCs) Those were also level and trend keys in 2004, 2003 and 2002. N2O emis-sion from Nitric Acid Production is in 2005 not a source since production stopped in the middle of 2004. The trends from year 1990 to 2005 for the two key sources are increasing emissions from Cement Production and from Substitutes for the Ozone Depleting Substances (HFCs and PFCs) (trend from 1995). As regards the level assessment, Cement Production contributes with 2.3% as in 2004 (1.9% in 2003 and 2.1% in 2002), and Substitutes for Ozone Depleting substances (HFCs and PFCs) with 1.3% (1.1% in 2004, 1.0% in 2003 and in 2002). Nitric Acid Production contrib-uted with 0.8% in 2004 (1.0% in 2003 and 1.1% in 2002).
For the ����� �� ���������� the analysis includes five sources. Of those are four keys to both level and trend as in 2004, while in 2003 they were all keys to both level and trend. In 2002, 2001 and 2000, only three of those sources were keys. These four key categories mentioned in order of falling contribution are direct N2O emissions from agriculture soils (4.7%), indirect N2O emissions from nitrogen used in agriculture (4.2%), CH4 from enteric fermentation (4.1%) and CH4 from manure manage-ment (1.6%). The emission estimates for the three most contributing sources represent a reduced emission from 1990 to 2005, while the fourth represents increasing emissions. According to the level assessment, these four sources are among the 12 most contributing sources, with direct N2O emissions from agriculture soils contributing 4.7% (in 2004 4.3%, in 2003 3.9%, in 2002 4.3% and in 2001 6.5%), indirect N2O emissions from nitrogen used in agriculture contributing 4.2% (in 2004 4.1%, in 2003 3.7%, in 2002 4.1% and in 2001 4.3%), CH4 from enteric fermentation 4.1% (in 2004 4.0%, in 2003 3.7%, in 2002 4.1% and in 2001 4.0%) and CH4 from manure management 1.6% (in 2004 1.5% and in 2003 1.3%). The emission estimates of N2O from manure management contribules 0.9% (in 2004 and 2003 0.8%).
In the ������������, one source – CH4 emissions from solid disposal of waste – is a key source with respect to level, while in previous analysis for 2001-2004 the source was key with respect to both level and trend. The emission estimates decrease over the period from 1990 to 2004, the contribution to national total being 1.6% in 2005 as in 2004 and 2003.
Energy CO2 Emission from stationary Combustion Coal CO2 24,077 14,568 0,228 0,23Energy Mobile combustion Road Transportation CO2 9,250 12,157 0,190 0,42Energy CO2 Emission from stationary Combustion Natural gas CO2 4,330 10,776 0,169 0,59Agriculture Direct N2O emissions from Agriculture soils N2O 4,224 2,976 0,047 0,63Agriculture Indirect N2O emissions from Nitrogen used in agriculture N2O 4,127 2,701 0,042 0,68Agriculture Enteric fermentation CH4 3,259 2,630 0,041 0,72Energy CO2 Emission from stationary Combustion Gas oil CO2 4,547 2,430 0,038 0,75Energy CO2 Emission from stationary Combustion Residual oil CO2 2,505 1,647 0,026 0,78Industrial Processes CO2 emissions from Cement production CO2 0,882 1,456 0,023 0,80Energy Mobile combustion agriculture CO2 1,272 1,085 0,017 0,82Waste Emission from Solid Waste Disposal sites CH4 1,335 1,059 0,017 0,84Agriculture CH4 from Manure management CH4 0,751 1,016 0,016 0,85Energy Mobile combustion other mobil and machinery/CO2 0,842 0,950 0,015 0,87Energy CO2 Emission from stationary Combustion Refinery gas CO2 0,806 0,873 0,014 0,88Energy CO2 Emission from stationary Combustion Petroleum coke CO2 0,410 0,859 0,013 0,89Industrial Processes Emission from substitutes for ODS (Consumption..) HFC and 0,218 0,819 0,013 0,91Energy CO2 Emission from stationary Combustion Plastic waste CO2 0,349 0,685 0,011 0,92Agriculture N2O from Manure management N2O 0,684 0,557 0,009 0,93Energy Mobile combustion Navigation CO2 0,554 0,543 0,008 0,93Energy Non-CO2 Emission from stationary Combustion CH4 0,121 0,520 0,008 0,94Energy Mobile combustion national fishing CO2 0,751 0,470 0,007 0,95Energy Fugitive emissions Oil and Natural Gas CO2 0,263 0,435 0,007 0,96Energy Mobile combustion Road Transportation N2O 0,122 0,429 0,007 0,96Energy Mobile combustion household and gardening CO2 0,138 0,297 0,005 0,97Energy Mobile combustion Military CO2 0,119 0,271 0,004 0,97Energy Non-CO2 Emission from stationary Combustion N2O 0,240 0,262 0,004 0,98Waste Emission from Waste Water Handling CH4 0,126 0,253 0,004 0,98Energy Mobile combustion Railways CO2 0,297 0,232 0,004 0,98Energy Mobile combustion Civil Aviation CO2 0,243 0,133 0,002 0,99Solvent and Other Product Use CO2 0,142 0,116 0,002 0,99Industrial Processes CO2 emissions from Lime production CO2 0,152 0,110 0,002 0,99Energy CO2 Emission from stationary Combustion Coke Oven Coke CO2 0,138 0,106 0,002 0,99Energy Fugitive emissions Oil and Natural Gas CH4 0,040 0,101 0,002 0,99Energy CO2 Emission from stationary Combustion LPG CO2 0,169 0,095 0,001 0,99Waste Emission from Waste Water Handling N2O 0,088 0,061 0,001 1,00Industrial Processes CO2 emissions from Limestone and Dolomite use CO2 0,018 0,061 0,001 1,00Energy Mobile combustion Road Transportation CH4 0,058 0,048 0,001 1,00Energy CO2 Emission from stationary Combustion Kerosene CO2 0,366 0,020 <0,001 1,00Energy Mobile combustion forestry CO2 0,036 0,017 <0,001 1,00Industrial Processes CO2 emissions Iron and Steel Production CO2 0,028 0,016 <0,001 1,00Energy Mobile combustion agriculture N2O 0,015 0,014 <0,001 1,00Industrial Processes CO2 emissions Glass/Glass Woll Production CO2 0,017 0,013 <0,001 1,00Industrial Processes SF6 from electrical equipment SF6 0,004 0,013 <0,001 1,00Energy Mobile combustion other mobil and machinery/N2O 0,011 0,012 <0,001 1,00Energy Mobile combustion Navigation N2O 0,010 0,010 <0,001 1,00Industrial Processes SF6 from other sources of SF6 SF6 0,068 0,009 <0,001 1,00Energy Mobile combustion national fishing N2O 0,015 0,009 <0,001 1,00Energy Mobile combustion household and gardening CH4 0,004 0,006 <0,001 1,00Energy Mobile combustion Military N2O 0,001 0,004 <0,001 1,00Industrial Processes CO2 emissions Catalysts/Fertilizers and Pesticides CO2 0,001 0,003 <0,001 1,00Energy Mobile combustion Civil Aviation N2O 0,003 0,002 <0,001 1,00Energy Fugitive emissions Oil and Natural Gas N2O 0,001 0,002 <0,001 1,00Energy Mobile combustion Railways N2O 0,003 0,002 <0,001 1,00Waste Gasification of biogas CO2 0,000 0,002 <0,001 1,00Industrial Processes CO2 emissions from Road paving with asphalt CO2 0,002 0,002 <0,001 1,00Energy Mobile combustion household and gardening N2O 0,001 0,001 <0,001 1,00Energy Mobile combustion agriculture CH4 0,002 0,001 <0,001 1,00Energy Mobile combustion other mobil and machinery/CH4 0,001 0,001 <0,001 1,00Energy Mobile combustion Navigation CH4 0,001 0,001 <0,001 1,00Energy Mobile combustion Military CH4 0,000 0,000 <0,001 1,00Energy Mobile combustion national fishing CH4 0,000 0,000 <0,001 1,00Energy Mobile combustion Railways CH4 0,000 0,000 <0,001 1,00Energy Mobile combustion forestry N2O 0,000 0,000 <0,001 1,00Energy Mobile combustion Civil Aviation CH4 0,000 0,000 <0,001 1,00Energy Mobile combustion forestry CH4 0,000 0,000 <0,001 1,00
�
$%���&��������'��������(�(�����������)�)�
*+���,������ ����)�%���������)����
*���������-������������������������.��������-�
Industrial Processes CO2 emissions from Asphalt roofing CO2 0,000 0,000 <0,001 1,00Waste Gasification of biogas N2O 0,000 0,000 <0,001 1,00Waste Gasification of biogas CH4 0,000 0,000 <0,001 1,00Energy CO2 Emission from stationary Combustion Brown Coal Bri CO2 0,011 0,000 <0,001 1,00Industrial Processes Nitric Acid Production N2O 1,043 0,000 <0,001 1,00Industrial Processes SF6 from magnesium Production SF6 0,036 0,000 <0,001 1,00
Total 69,33 63,95 1,00
(1) The base year is 1995 for HFC, PFC and SF6; and 1990 for the other greenhouse gases. The base year is unadjusted to electricity trade.
Energy CO2 Emission from stationary Combustion Coal CO2 24,08 14,57 0,1295 25,9 25,9Energy CO2 Emission from stationary Combustion Natural gas CO2 4,33 10,78 0,1150 23,0 49,0Energy Mobile combustion Road Transportation CO2 9,25 12,16 0,0614 12,3 61,3Energy CO2 Emission from stationary Combustion Gas oil CO2 4,55 2,43 0,0299 6,0 67,3Agriculture Indirect N2O emissions from Nitrogen used in agriculture N2O 4,13 2,70 0,0187 3,8 71,0Industrial Processes Nitric Acid Production N2O 1,04 0,00 0,0163 3,3 74,3Agriculture Direct N2O emissions from Agriculture soils N2O 4,22 2,98 0,0156 3,1 77,4Energy CO2 Emission from stationary Combustion Residual oil CO2 2,51 1,65 0,0113 2,3 79,7Industrial Processes CO2 emissions from Cement production CO2 0,88 1,46 0,0109 2,2 81,8Industrial Processes Emission from substitutes for ODS (Consumption..) HFC and 0,22 0,82 0,0105 2,1 83,9Energy CO2 Emission from stationary Combustion Petroleum coke CO2 0,41 0,86 0,0082 1,6 85,6Energy Non-CO2 Emission from stationary Combustion CH4 0,12 0,52 0,0069 1,4 87,0Agriculture Enteric fermentation CH4 3,26 2,63 0,0064 1,3 88,2Energy CO2 Emission from stationary Combustion Plastic waste CO2 0,35 0,69 0,0062 1,2 89,5Agriculture CH4 from Manure management CH4 0,75 1,02 0,0055 1,1 90,6Energy CO2 Emission from stationary Combustion Kerosene CO2 0,37 0,02 0,0054 1,1 91,6Energy Mobile combustion Road Transportation N2O 0,12 0,43 0,0054 1,1 92,7Energy Mobile combustion national fishing CO2 0,75 0,47 0,0038 0,8 93,5Energy Fugitive emissions Oil and Natural Gas CO2 0,26 0,43 0,0032 0,7 94,1Energy Mobile combustion other mobil and machinery/CO2 0,84 0,95 0,0029 0,6 94,7Waste Emission from Solid Waste Disposal sites CH4 1,34 1,06 0,0029 0,6 95,3Energy Mobile combustion household and gardening CO2 0,14 0,30 0,0029 0,6 95,9Energy Mobile combustion Military CO2 0,12 0,27 0,0027 0,5 96,4Waste Emission from Waste Water Handling CH4 0,13 0,25 0,0023 0,5 96,9Energy CO2 Emission from stationary Combustion Refinery gas CO2 0,81 0,87 0,0022 0,4 97,3Energy Mobile combustion Civil Aviation CO2 0,24 0,13 0,0015 0,3 97,6Energy Mobile combustion agriculture CO2 1,27 1,08 0,0015 0,3 97,9Agriculture N2O from Manure management N2O 0,68 0,56 0,0013 0,3 98,2Energy Fugitive emissions Oil and Natural Gas CH4 0,04 0,10 0,0011 0,2 98,4Energy CO2 Emission from stationary Combustion LPG CO2 0,17 0,09 0,0010 0,2 98,6Industrial Processes SF6 from other sources of SF6 SF6 0,07 0,01 0,0009 0,2 98,8Industrial Processes CO2 emissions from Limestone and Dolomite use CO2 0,02 0,06 0,0007 0,1 98,9Energy Mobile combustion Railways CO2 0,30 0,23 0,0007 0,1 99,1Energy Non-CO2 Emission from stationary Combustion N2O 0,24 0,26 0,0007 0,1 99,2Industrial Processes SF6 from magnesium Production SF6 0,04 0,00 0,0006 0,1 99,3Energy Mobile combustion Navigation CO2 0,55 0,54 0,0005 0,1 99,4Industrial Processes CO2 emissions from Lime production CO2 0,15 0,11 0,0005 0,1 99,6Energy CO2 Emission from stationary Combustion Coke Oven Coke CO2 0,14 0,11 0,0004 0,1 99,6Waste Emission from Waste Water Handling N2O 0,09 0,06 0,0003 0,1 99,7Energy Mobile combustion forestry CO2 0,04 0,02 0,0003 0,1 99,7Solvent and Other Product Use CO2 0,14 0,12 0,0003 0,1 99,8Industrial Processes CO2 emissions Iron and Steel Production CO2 0,03 0,02 0,0002 0,0 99,8Energy CO2 Emission from stationary Combustion Brown Coal Bri CO2 0,01 0,00 0,0002 0,0 99,9Industrial Processes SF6 from electrical equipment SF6 0,00 0,01 0,0002 0,0 99,9Energy Mobile combustion Road Transportation CH4 0,06 0,05 0,0001 0,0 99,9Energy Mobile combustion national fishing N2O 0,01 0,01 0,0001 0,0 99,9Industrial Processes CO2 emissions Glass/Glass Woll Production CO2 0,02 0,01 0,0001 0,0 99,9Energy Mobile combustion Military N2O 0,00 0,00 <0,0001 0,0 100,0Energy Mobile combustion other mobil and machinery/N2O 0,01 0,01 <0,0001 0,0 100,0Energy Mobile combustion household and gardening CH4 0,00 0,01 <0,0001 0,0 100,0Industrial Processes CO2 emissions Catalysts/Fertilizers and Pesticides CO2 0,00 0,00 <0,0001 0,0 100,0Waste Gasification of biogas CO2 0,00 0,00 <0,0001 0,0 100,0Energy Fugitive emissions Oil and Natural Gas N2O 0,00 0,00 <0,0001 0,0 100,0Energy Mobile combustion household and gardening N2O 0,00 0,00 <0,0001 0,0 100,0Energy Mobile combustion agriculture CH4 0,00 0,00 <0,0001 0,0 100,0Energy Mobile combustion Civil Aviation N2O 0,00 0,00 <0,0001 0,0 100,0Energy Mobile combustion Railways N2O 0,00 0,00 <0,0001 0,0 100,0Energy Mobile combustion forestry CH4 0,00 0,00 <0,0001 0,0 100,0Industrial Processes CO2 emissions from Road paving with asphalt CO2 0,00 0,00 <0,0001 0,0 100,0Energy Mobile combustion other mobil and machinery/CH4 0,00 0,00 <0,0001 0,0 100,0Energy Mobile combustion Military CH4 0,00 0,00 <0,0001 0,0 100,0Energy Mobile combustion Navigation N2O 0,01 0,01 <0,0001 0,0 100,0Energy Mobile combustion Navigation CH4 0,00 0,00 <0,0001 0,0 100,0Energy Mobile combustion national fishing CH4 0,00 0,00 <0,0001 0,0 100,0Energy Mobile combustion Railways CH4 0,00 0,00 <0,0001 0,0 100,0
����� &��� ��� ���� '���� �������!
���� ����(��� � ����� ��������� ��)���*����(!
�
+'�� ,���� ���-����� �././�� �� �������!
Energy Mobile combustion agriculture N2O 0,02 0,01 <0,0001 0,0 100,0Waste Gasification of biogas N2O 0,00 0,00 <0,0001 0,0 100,0Energy Mobile combustion forestry N2O 0,00 0,00 <0,0001 0,0 100,0Industrial Processes CO2 emissions from Asphalt roofing CO2 0,00 0,00 <0,0001 0,0 100,0Energy Mobile combustion Civil Aviation CH4 0,00 0,00 <0,0001 0,0 100,0Waste Gasification of biogas CH4 0,00 0,00 <0,0001 0,0 100,0
EnergyCO2 Emission from stationary Combustion Coal CO2 Yes Level, Trend ��������
CO2 Emission from stationary Combustion Brown Coal Bri CO2 NoCO2 Emission from stationary Combustion Coke Oven Coke CO2 NoCO2 Emission from stationary Combustion Petroleum coke CO2 Yes Level, Trend ��������
CO2 Emission from stationary Combustion Plastic waste CO2 Yes Level, Trend ��������
CO2 Emission from stationary Combustion Residual oil CO2 Yes Level, Trend ��������
CO2 Emission from stationary Combustion Gas oil CO2 Yes Level, Trend ��������
CO2 Emission from stationary Combustion Kerosene CO2 Yes TrendCO2 Emission from stationary Combustion Orimulsion CO2 NoCO2 Emission from stationary Combustion Natural gas CO2 Yes Level, Trend ��������
CO2 Emission from stationary Combustion LPG CO2 NoCO2 Emission from stationary Combustion Refinery gas CO2 Yes Level ��������
Mobile combustion Civil Aviation CO2 NoMobile combustion Road Transportation CO2 Yes Level, Trend ��������
Mobile combustion Railways CO2 NoMobile combustion Navigation CO2 Yes levelMobile combustion Military CO2 NoMobile combustion national fishing CO2 Yes Level, Trend ��������
Mobile combustion agriculture CO2 Yes Level ��������
Mobile combustion forestry CO2 NoMobile combustion other mobil and machinery/industry CO2 Yes Level, Trend ��������
Mobile combustion household and gardening CO2 NoFugitive emissions Oil and Natural Gas CO2 Yes Trend ��������
Non-CO2 Emission from stationary Combustion CH4 Yes Level, Trend ��������
Mobile combustion Civil Aviation CH4 NoMobile combustion Road Transportation CH4 NoMobile combustion Railways CH4 NoMobile combustion Navigation CH4 NoMobile combustion Military CH4 NoMobile combustion national fishing CH4 NoMobile combustion agriculture CH4 NoMobile combustion forestry CH4 NoMobile combustion other mobil and machinery/industry CH4 NoMobile combustion household and gardening CH4 NoFugitive emissions Oil and Natural Gas CH4 NoNon-CO2 Emission from stationary Combustion N2O NoMobile combustion Civil Aviation N2O NoMobile combustion Road Transportation N2O Yes TrendMobile combustion Railways N2O NoMobile combustion Navigation N2O NoMobile combustion Military N2O NoMobile combustion national fishing N2O NoMobile combustion agriculture N2O NoMobile combustion forestry N2O NoMobile combustion other mobil and machinery/industry N2O NoMobile combustion household and gardening N2O NoFugitive emissions Oil and Natural Gas N2O No
Industrial ProcessesCO2 emissions from Cement production CO2 Yes Level, Trend ��������
CO2 emissions from Lime production CO2 NoCO2 emissions from Limestone and Dolomite use CO2 NoCO2 emissions from Asphalt roofing CO2 NoCO2 emissions from Road paving with asphalt CO2 NoCO2 emissions Glass/Glass Woll Production CO2 NoCO2 emissions Catalysts/Fertilizers and Pesticides CO2 NoCO2 emissions Iron and Steel Production CO2 NoNitric Acid Production N2O Yes Trend ��������
SF6 from magnesium Production SF6 NoSF6 from electrical equipment SF6 NoSF6 from other sources of SF6 SF6 NoEmission from substitutes for ODS HFC and PYes Level, Trend ��������
Solvent and Other Product UseSolvent and Other Product Use CO2 No
Direct N2O emissions from Agriculture soils N2O Yes Level, Trend ��������
Indirect N2O emissions from Nitrogen used in agriculture N2O Yes Level, Trend ��������
WasteEmission from Solid Waste Disposal sites CH4 Yes Level ��������
Emission from Waste Water Handling CH4 NoEmission from Waste Water Handling N2O NoGasification of biogas CO2 NoGasification of biogas N2O NoGasification of biogas CH4 No
This annex is a sector report for stationary combustion that includes mo-re background data and a more detailed methodology description than included in the main NIR report.
-��������
3A-1 INTRODUCTION 357
3A-2 METHODOLOGY AND REFERENCES 358
3A-2.1 Emission source categories 358
3A-2.2 Large point sources 360
3A-2.3 Area sources 361
3A-2.4 Activity rates, fuel consumption 361
3A-2.5 Emission factors 362
3A-2.5.1 CO2 363
3A-2.5.2 CH4 368
3A-2.5.3 N2O 372
3A-2.5.4 SO2, NOX, NMVOC and CO 374
3A-2.6 Disaggregation to specific industrial subsectors 374
3A-3 FUEL CONSUMPTION DATA 376
3A-4 GREENHOUSE GAS EMISSION 379
3A-4.1 CO2 381
3A-4.2 CH4 385
3A-4.3 N2O 387
3A-5 SO2, NOX, NMVOC AND CO 389
3A-5.1 SO2 389
3A-5.2 NOX 391
3A-5.3 NMVOC 393
3A-5.4 CO 395
3A-6 QA/QC AND VALIDATION 398
3A-6.1 Reference approach 404
3A-6.2 Key source analysis 406
3A-7 UNCERTAINTY 407
3.A-7.1 Methodology 407
3A-7.1.1 Greenhouse gases 407
356
3A-7.1.2 Other pollutants 408
3A-7.2 Results 408
3A-8 IMPROVEMENTS/RECALCULATIONS SINCE REPORTING IN 2004 410
3A-9 FUTURE IMPROVEMENTS 411
3A-10 CONCLUSION 412
REFERENCES 414
APPENDICES (3A-1 to 3A-10) 417
357
�� �����!�������
The Danish atmospheric emission inventories are prepared on an annual basis and the results are reported to the ��� �������� � �� �� � ��������� ��� ��� (UNFCCC or Climate Convention) and to the UNECE � �� �� � � � ���� ��� ��� ��� ��������� ������ (LRTAP Conven-tion). Furthermore, a greenhouse gas emission inventory is reported to the EU, due to the EU – as well as the individual Member States –party to the Climate Convention. The Danish atmospheric emission inventories are calculated by the Danish National Environmental Research Institute (NERI).
This annex provides a summary of the emission inventories for station-ary combustion reported to the Climate Convention and background do-cumentation for the estimates. Stationary combustion plants include po-wer plants, district heating plants, non-industrial and industrial combus-tion plants, industrial process burners, petroleum-refining plants, as well as combustion in oil/gas extraction and in pipeline compressors. Emis-sions from flaring in oil/gas production and from flaring carried out in refineries are not covered in this annex.
This annex presents detailed emission inventories and time-series for emissions from stationary combustion plants. Furthermore, emissions from stationary combustion plants are compared with total Danish emis-sions. The methodology and references for the emission inventories for stationary combustion plants are described. Furthermore, uncertainty es-timates are provided.
358
�� 1�� �!���"����!������������
The Danish emission inventory is based on the CORINAIR (CORe IN-ventory on AIR emissions) system, which is a European programme for air emission inventories. CORINAIR includes methodology structure and software for inventories. The methodology is described in the EMEP/Corinair Emission Inventory Guidebook 3rd edition, prepared by the UNECE/EMEP Task Force on Emissions Inventories and Projections (EMEP/Corinair 2004). Emission data are stored in an Access database, from which data are transferred to the reporting formats.
The emissions inventory for stationary combustion is based on activity rates from the Danish energy statistics. General emission factors for vari-ous fuels, plants and sectors have been determined. Some large plants, such as power plants, are registered individually as large point sources and plant-specific emission data are used.
�(�� /������������������"������
In the Danish emission database, all activity rates and emissions are de-fined in SNAP sector categories (Selected Nomenclature for Air Pollu-tion) according the CORINAIR system. The emission inventories are pre-pared from a complete emission database based on the SNAP sectors. Aggregation to the sector codes used for the Climate Convention is ba-sed on a correspondence list between SNAP and IPCC enclosed in Ap-pendix 3A-2.
The sector codes applied in the reporting activity will be referred to as IPCC sectors. The IPCC sectors define 6 main source categories, listed in Table 3A-1, and a number of subcategories. Stationary combustion is part of the IPCC ector 1, � ����. Table 3A-2 presents subsectors in the IPCC energy sector. The table also presents the sector in which the NERI documentation is included. Though industrial combustion is part of sta-tionary combustion, detailed documentation for some of the specific in-dustries is discussed in the industry chapters/annexes. Stationary com-bustion is defined as combustion activities in the SNAP sectors 01-03.
�������� IPCC main sectors.
1. Energy
2. Industrial Processes
3. Solvent and Other Product Use
4. Agriculture
5. Land-Use Change and Forestry
6. Waste
359
�������� IPCC source categories for the energy sector.
IPCC id IPCC sector name NERI documentation
1 Energy Stationary combustion, Transport, Fugitive, Indus-try
1A Fuel Combustion Activities Stationary combustion, Transport, Industry
1A1 Energy Industries Stationary combustion
1A1a Electricity and Heat Production Stationary combustion
1A1b Petroleum Refining Stationary combustion
1A1c Solid Fuel Transf./Other Energy Industries Stationary combustion
1A2 Fuel Combustion Activities/Industry (ISIC) Stationary combustion, Transport, Industry
1A2a Iron and Steel Stationary combustion, Industry
1A2b Non-Ferrous Metals Stationary combustion, Industry
1A2c Chemicals Stationary combustion, Industry
1A2d Pulp, Paper and Print Stationary combustion, Industry
1A2e Food Processing, Beverages and Tobacco Stationary combustion, Industry
1A2f Other (please specify) Stationary combustion, Transport, Industry
1A3 Transport Transport
1A3a Civil Aviation Transport
1A3b Road Transportation Transport
1A3c Railways Transport
1A3d Navigation Transport
1A3e Other (please specify) Transport
1A4 Other Sectors Stationary combustion, Transport
The emission sources �% and �(! however, also include emissions from transport subsectors. The emission source �% includes emissions from some off-road machinery in the industry. The emission source �( includes off-road machinery in agriculture, forestry and house-hold/gardening. Further emissions from national fishing are included in subsector �(.
The emission and fuel consumption data included in the tables and fig-ures in this annex only include emissions originating from stationary combustion plants of a given IPCC sector. The IPCC sector codes have been applied unchanged, but some sector names have been changed to reflect the stationary combustion element of the source.
The CO2 from calcination is not part of the energy sector. This emission is included in the IPCC sector 2, Industrial Processes.
�(�� 2��"����������������
Large emission sources such as power plants, industrial plants and refin-eries are included as large point sources in the Danish emission database. Each point source may consist of more than one part, e.g. a power plant with several units. By registering the plants as point sources in the data-base it is possible to use plant-specific emission factors.
In the inventory for the year 2005, 75 stationary combustion plants are specified as large point sources. These point sources include:
• Power plants and decentralised CHP plants (combined heat and power plants)
• Municipal waste incineration plants • Large industrial combustion plants • Petroleum refining plants The criteria for selection of point sources consist of the following:
• All centralised power plants, including smaller units. • All units with a capacity of above 25 MWe. • All district heating plants with an installed effect of 50 MW or above
and a significant fuel consumption • All waste incineration plants included under the Danish law "Bek-
endtgørelse om visse listevirksomheders pligt til at udarbejde grønt regnskab".
• Industrial plants • With an installed effect of 50 MW or above and significant fuel
consumption.
361
• With a significant process-related emission.
The fuel consumption of stationary combustion plants registered as large point sources is 341 PJ (2005). This corresponds to 64% of the overall fuel consumption for stationary combustion.
A list of the large point sources for 2005 and the fuel consumption rates is provided in Appendix 3A-5. The number of large point sources regis-tered in the databases increased from 1990 to 2005.
The emissions from a point source are based either on plant-specific emission data or, if plant specific-data are not available, on fuel con-sumption data and the general Danish emission factors. Appendix 3A-5 shows which of the emission data for large point sources are plant-specific and which are based on emission factors.
SO2 and NOX emissions from large point sources are often plant-specific based on emission measurements. Emissions of CO and NMVOC are al-so plant-specific for some plants. Plant-specific emission data are ob-tained from:
• Annual environmental reports • Annual plant-specific reporting of SO2 and NOX from power plants
>25MWe prepared for the Danish Energy Authority due to Danish legislatory requirements
• Emission data reported by Elsam and E2, the two major electricity suppliers
• Emission data reported by industrial plants Annual environmental reports for the plants include a considerable number of emission datasets. Emission data from annual environmental reports are, in general, based on emission measurements, but some emis-sions have potentially been calculated from general emission factors.
If plant-specific emission factors are not available, general area source emission factors are used. Emissions of the greenhouse gases (CO2, CH4 and N2O) from the large point sources are all based on the area source emission factors.
�()� �������������
Fuels not combusted in large point sources are included as sector-specific area sources in the emission database. Plants such as residential boilers, small district heating plants, small CHP plants and some industrial boil-ers are defined as area sources. Emissions from area sources are based on fuel consumption data and emission factors. Further information on emission factors is provided below.
�(%� ����$���������3�����������������
The fuel consumption rates are based on the official Danish energy statis-tics prepared by the Danish Energy Authority. The Danish Energy Au-thority aggregates fuel consumption rates to SNAP sector categories
362
(DEA 2006a). Some fuel types in the official Danish energy statistics are added to obtain a less detailed fuel aggregation level, see Appendix 3A-7. The calorific values on which the energy statistics are based are also enclosed in Appendix 3A-7.
The fuel consumption of the IPCC sector �%�&� �'�"���� �� � ���������� ��" ����"�� (corresponding to SNAP sector +,�������� �� ��� �'�"����� ��� ��������- is not disaggregated into specific industries in the NERI emission database. Disaggregation into specific industries is estimated for the reporting to the Climate Convention. The disaggregation of fuel consumption and emissions from the industrial sector is discussed in Chapter 3.6.
Both traded and non-traded fuels are included in the Danish energy sta-tistics. Thus, for example, estimation of the annual consumption of non-traded wood is included.
Petroleum coke purchased abroad and combusted in Danish residential plants (border trade of 628 TJ) is added to the apparent consumption of petroleum coke and the emissions are included in the inventory.
The Danish Energy Authority (DEA) compiles a database for the fuel consumption of each district heating and power-producing plant, based on data reported by plant operators. The fuel consumption of large point sources specified in the Danish emission database is based on the DEA database (DEA 2006c).
The fuel consumption of area sources is calculated as total fuel consump-tion minus fuel consumption of large point sources.
Emissions from the non-energy use of fuels have not been included in the Danish inventory, to date, but the non-energy use of fuels is, how-ever, included in the reference approach for Climate Convention report-ing. The Danish energy statistics include three fuels used for non-energy purposes: bitumen, white spirit and lube oil. The fuels used for non-energy purposes add up to about 2% of the total fuel consumption in Denmark.
In Denmark, all municipal waste incineration is utilised for heat and po-wer production. Thus, incineration of waste is included as stationary combustion in the IPCC Energy sector (source categories � , �% and �(-.
Fuel consumption data are presented in Chapter 3.
�(�� /����������������
For each fuel and SNAP category (sector and e.g. type of plant) a set of general area source emission factors has been determined. The emission factors are either nationally referenced or based on the international guidebooks: EMEP/Corinair Guidebook (EMEP/Corinair, 2004) and IPCC Reference Manual (IPCC 1996).
363
A complete list of emission factors, including time-series and references, is provided in Appendix 3A-4.
�(�(�� -.��
The CO2 emission factors applied for 2005 are presented in Table 3A-3. For municipal waste and natural gas, time-series have been estimated. For all other fuels the same emission factor is applied for 1990-2005.
In reporting for the Climate Convention, the CO2 emission is aggregated to five fuel types: Solid fuel, Liquid fuel, Gas, Biomass and Other fuels. The correspondence list between the NERI fuel categories and the IPCC fuel categories is also provided in Table 3A-3.
Only emissions from fossil fuels are included in the national total CO2 emission. The biomass emission factors are also included in the table, be-cause emissions from biomass are reported to the Climate Convention as a memo item.
The CO2 emission from the incineration of municipal waste (94,5 + 17,6 kg/GJ) is divided into two parts: the emission from combustion of the plastic content of the waste, which is included in the national total, and the emission from combustion of the rest of the waste – the biomass part, which is reported as a memo item. In the IPCC reporting, the CO2 emis-sion from combustion of the plastic content of the waste is reported in the fuel category, )�����'����. However, this split is not applied in either in the case of fuel consumption or other emissions, because it is only re-levant for CO2. Thus, the full consumption of municipal waste is in-cluded in the fuel category, .�����! and non-CO2 emissions from mu-nicipal waste combustion are also included in full in the .������ cate-gory.
The CO2 emission factors have been confirmed by the two major power plant operators, both directly (Christiansen, 1996 and Andersen, 1996) and indirectly, by applying the NERI emission factors in the annual en-vironmental reports for the large power plants and by accepting use of the NERI factors in Danish legislation.
In just adapted legislation (Law no. 493 2004), operators of large power plants are obliged to verify the applied emission factors, the input from the large power plants has not given reason to change the CO2 emission factors.
364
�������� CO2 emission factors 2005.
-����The emission factor 95 kg/GJ is based on Fenhann & Kilde 1994. The CO2 emission factors have been confirmed by the two major power plant operators in 1996 (Christiansen 1996 and Andersen 1996). Elsam recon-firmed the factor in 2001 (Christiansen 2001). The same emission factor is applied for 1990-2005.
4��5�������&��6�������The emission factor 94.6 kg/GJ is based on a default value from the IPCC guidelines assuming full oxidation. The default value in the IPCC guide-lines is 25.8 t C/TJ, corresponding to 25.8·(12+2·16)/12 = 94.6 kg CO2/GJ assuming full oxidation. The same emission factor is applied for 1990-2005.
-� ���$����� ��The emission factor 108 kg/GJ is based on a default value from the IPCC guidelines assuming full oxidation. The default value in the IPCC guide-lines is 29.5 t C/TJ, corresponding to 29.5·(12+2·16)/12 = 108 kg CO2/GJ assuming full oxidation. The same emission factor is applied for 1990-2005.
+���������� ��The emission factor 92 kg/GJ has been estimated by SK Energy (a former major power plant operator in eastern Denmark) in 1999 based on a fuel analysis carried out by dk-Teknik in 1993 (Bech 1999). The emission fac-tor level was confirmed by a new fuel analysis which, however, is con-sidered confidential. The same emission factor is applied for 1990-2005.
7��!�The emission factor for wood, 102 kg/GJ, refers to Fenhann & Kilde 1994. The factor is based on the interval stated in a former edition of the EMEP/Corinair Guidebook and the actual value is the default value from the Collector database. The same emission factor is applied for 1990-2005.
Fuel Emission factor Unit Reference type IPCC fuel
Biomass Fossil fuel Category
Coal 95 kg/GJ Country-specific Solid
Brown coal briquettes 94.6 kg/GJ IPCC reference manual Solid
Municipal waste 94.5 17.6 kg/GJ Country-specific Biomass / Other fuels
Straw 102 kg/GJ Country-specific Biomass
Residual oil 78 kg/GJ Corinair Liquid
Gas oil 74 kg/GJ Corinair Liquid
Kerosene 72 kg/GJ Corinair Liquid
Fish & rape oil 74 kg/GJ Country-specific Biomass
Orimulsion 80 kg/GJ Country-specific Liquid
Natural gas 56.96 kg/GJ Country-specific Gas
LPG 65 kg/GJ Corinair Liquid
Refinery gas 56.9 kg/GJ Country-specific Liquid
Biogas 83.6 kg/GJ Country-specific Biomass
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1���������5�����The CO2 emission from incineration of municipal waste is divided into two parts: the emission from combustion of the plastic content of the waste, which is included in the national total, and the emission from combustion of the rest of the waste – the biomass part – which is re-ported as a memo item.
The plastic content of waste was estimated to be 6.6 w/w% in 2003 (Hul-gaard 2003). The weight share, lower heating values and CO2 emission factors for different plastic types are estimated by Hulgaard in 2003 (Ta-ble 3A-4). The total weight share for plastic and for the various plastic types is assumed to be the same for all years (NERI assumption).
�������� Data for plastic waste in Danish municipal waste (Hulgaard 2003)1)2).
Hulgaard 2003 refers to: 1) TNO report 2000/119, Eco-efficiency of recovery scenarios of plastic packaging, Appendices, July 2001 by P.G. Eggels, A.M.M. Ansems, B.L. van der Ven, for Association of Plastic Manufacturers in Europe 2) Kost, Thomas, Brennstofftechnische Charakterisierung von Haushaltabfällen, Technische Universität Dresden, Eigenverlag des Forums für Abfallwirtschaft und Altlasten e.V., 2001
Based on emission measurements on 5 municipal waste incineration plants (Jørgensen & Johansen, 2003), the total CO2 emission factor for municipal waste incineration has been determined to be 112.1 kg/GJ. The CO2 emission from the biomass part is the total CO2 emission minus the CO2 emission from the plastic part.
Thus, in 2003, the CO2 emission factor for the plastic content of waste was estimated to be 185g/kg municipal waste (Table 3A-4). The CO2 emission per GJ of waste is calculated based on the lower heating values for waste listed in Table 3A-5 (DEA 2006b). It has been assumed that the plastic content as a percentage (weight) is constant, resulting in a de-creasing energy percentage since the lower heating value (LHV) is in-creasing. However, the increasing LHV may be a result of an increase in the plastic content in the municipal waste. Time-series for the CO2 emis-sion factor for plastic content in waste are included in Table 3A-5.
Emission data from four waste incineration plants (Jørgensen & Johansen 2003) demonstrate the fraction of the carbon content of the waste not oxidised to be approximately 0.3%. The un-oxidised fraction of the car-
Plastic type Mass share of plastic in municipal waste
in Denmark
Lower heating value
of plastic
Energy content of
plastic
CO2 emis-sion factor for plastic
CO2 emission
factor
kg plastic/
kg municipal waste
% of plastic
MJ/kg plastic MJ/kg
municipal waste
g/MJ plastic g/kg
municipal waste
PE 0.032 48 41 1.312 72.5 95
PS/EPS 0.02 30 37 0.74 86 64
PVC 0.007 11 18 0.126 79 10
Other
(PET, PUR, PC, POM,ABS, PA etc.)
0.007 11 24 0.168 95 16
Total 0.066 100 35.5 2.346 78.7 185
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bon content is assumed to originate from the biomass content, and all carbon originating from plastic is assumed to be oxidised.
�������� CO2 emission factor for municipal waste, plastic content and biomass content.
1) DEA 2005b
2) Based on data from Jørgensen & Johansen 2003
3) From Table 3A-4
0���5�The emission factor for straw, 102 kg/GJ, is from Fenhann & Kilde, 1994. The factor is based on the interval stated in the EMEP/Corinair Guide-book (EMEP/Corinair, 2004) and the actual value is the default value from the Collecter database. The same emission factor is applied for 1990-2005.
����!��������The emission factor 78 kg/GJ comes from Fenhann & Kilde, 1994. The factor is based on the interval stated in the EMEP/Corinair Guidebook (EMEP/Corinair; 2004). The factor is slightly higher than the IPCC de-fault emission factor for residual fuel oil (77,4 kg/GJ assuming full oxi-dation). The CO2 emission factors have been confirmed by the two major power plant operators in 1996 (Christiansen 1996 and Andersen 1996). The same emission factor is applied for 1990-2005.
*�������The emission factor 74 kg/GJ refers to Fenhann & Kilde 1994. The factor is based on the interval stated in the EMEP/Corinair Guidebook (EMEP/Corinair, 2004). The factor agrees with the IPCC default emission factor for gas oil (74,1 kg/GJ assuming full oxidation). The CO2 emission factors have been confirmed by the two major power plant operators in 1996 (Christiansen 1996 and Andersen 1996). The same emission factor is applied for 1990-2005.
Year Lower heating value of munici-
pal waste 1)
[GJ/Mg]
Plastic
content
[% of energy]
CO2 emission factor for plastic 3)
[g/kg waste]
CO2 emission factor for
plastic
[kg/GJ waste]
CO2 emission factor for mu-nicipal waste,
total 2)
[kg/GJ waste]
CO2 emission factor for bio-
mass content of waste
[kg/GJ waste]
1990 8.20 28.6 185 22.5 112.1 89.6
1991 8.20 28.6 185 22.5 112.1 89.6
1992 9.00 26.1 185 20.5 112.1 91.6
1993 9.40 25.0 185 19.6 112.1 92.5
1994 9.40 25.0 185 19.6 112.1 92.5
1995 10.00 23.5 185 18.5 112.1 93.6
1996 10.50 22.3 185 17.6 112.1 94.5
1997 10.50 22.3 185 17.6 112.1 94.5
1998 10.50 22.3 185 17.6 112.1 94.5
1999 10.50 22.3 185 17.6 112.1 94.5
2000 10.50 22.3 185 17.6 112.1 94.5
2001 10.50 22.3 185 17.6 112.1 94.5
2002 10.50 22.3 185 17.6 112.1 94.5
2003 10.50 22.3 185 17.6 112.1 94.5
2004 10.50 22.3 185 17.6 112.1 94.5
2005 10.50 22.3 185 17.6 112.1 94.5
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���������The emission factor 72 kg/GJ refers to Fenhann & Kilde 1994. The factor agrees with the IPCC default emission factor for other kerosene (71.9 kg/GJ assuming full oxidation). The same emission factor is applied for 1990-2005.
8�� �9����������The emission factor is assumed to be the same as for gas oil – 74 kg/GJ. The consumption of fish and rape oil is relatively low.
.���������The emission factor 80 kg/GJ refers to the Danish Energy Authority (DEA 2004). The IPCC default emission factor is almost the same: 80.,7 kg/GJ assuming full oxidation. The CO2 emission factors have been con-firmed by the only major power plant operator using orimulsion (Ander-sen 1996). The same emission factor is applied for 1990-2005.
��������"���The emission factor for natural gas is estimated by the Danish gas trans-mission company, Energinet.dk. Only natural gas from the Danish gas fields is utilised in Denmark. The calculation is based on gas analysis carried out daily by Energinet.dk. Energinet.dk and the Danish Gas Technology Centre have calculated emission factors for 2000-2005. The emission factor applied for 1990-1999 refers to Fenhann & Kilde 1994. This emission factor was confirmed by the two major power plant opera-tors in 1996 (Christiansen 1996 and Andersen 1996). The time-series for the CO2 emission factors is provided in Table 3A-6.
2+*�The emission factor 65 kg/GJ refers to Fenhann & Kilde 1994. The emis-sion factor is based on the EMEP/Corinair Guidebook (EMEP/Corinair, 2004). The emission factor is somewhat higher than the IPCC default emission factor (63 kg/GJ assuming full oxidation). The same emission factor is applied for 1990-2005.
���������"���The emission factor applied for refinery gas is the same as the emission factor for natural gas 1990-1999. The emission factor is within the inter-val of the emission factor for refinery gas stated in the EMEP/Corinair Guidebook (EMEP/Corinair, 2004). The same emission factor is applied for 1990-2005.
4��"���The emission factor 83,6 kg/GJ is based on a biogas with 65% (vol.) CH4 and 35% (vol.) CO2. The Danish Gas Technology Centre has stated that
368
this is typical manure-based biogas as utilised in stationary combustion plants (Kristensen 2001). The same emission factor is applied for 1990-2005.
�(�(�� -:��
The CH4 emission factors applied for 2005 are presented in Table 3A-7. In general, the same emission factors have been applied for 1990-2005. However, time-series have been estimated for both natural gas fuelled engines and biogas fuelled engines.
Emission factors for gas engines, gas turbines and CHP plants combust-ing wood, straw or municipal waste all refer to emission measurements carried out on Danish plants (Nielsen & Illerup 2003). Other emission factors refer to the EMEP/Corinair Guidebook (EMEP/Corinair, 2004).
Gas engines combusting natural gas or biogas contribute much more to the total CH4 emission than other stationary combustion plants. The rela-tively high emission factor for gas engines is well-documented and fur-ther discussed below.
369
�������� CH4 emission factors 1990-2005.
1) 2004 emission factor. Time-series is shown below
-:+��������A considerable portion of the electricity production in Denmark is based on decentralised CHP plants and well-documented emission factors for these plants are, therefore, of importance. In a project carried out for the electricity transmission company in Western Denmark, Eltra, emission factors have been estimated for CHP plants <25MWe. The work was re-ported in 2003 (Nielsen & Illerup 2003) and the results have been fully implemented.
Gas engines: 010105, 010505, 030105, 020105, 020304
1)
323
Nielsen & Illerup 2003
BIOGAS 1A1a, 1A2f, 1A4a, 1A4c all other 4 EMEP/Corinair 2004
370
The work included municipal waste incineration plants, CHP plants combusting wood and straw, natural gas and biogas-fuelled (reciprocat-ing) engines, and natural gas fuelled gas turbines. CH4 emission factors for these plants all refer to Nielsen & Illerup, 2003. The estimated emis-sion factors were based on existing emission measurements as well as on emission measurements carried out within the project. The number of emission datasets was comprehensive. Emission factors for subgroups of each plant type were estimated, e.g. the CH4 emission factor for different gas engine types has been determined.
The emission factor for natural gas engines was determined as 520 g/GJ in 2000 and the same emission factor has been applied for 2001 - 2005. The emission factor for natural gas engines was based on 291 emission measurements in 114 different plants. The plants from which emission measurements were available represented 44% of the total gas consump-tion in gas engines (year 2000). The emission factor was estimated based on fuel consumption for each gas engine type and the emission factor for each engine type. The majority of emission measurements that were not performed within the project related solely to the emission of total un-burned hydrocarbon (CH4 + NMVOC). A constant disaggregation factor was estimated based on a number of emission measurements including both CH4 and NMVOC.
The emission factor for lean-burn gas engines is relatively high, espe-cially for pre-chamber engines, which account for more than half the gas consumption in Danish gas engines. However, the emission factors for different pre-chamber engine types differ considerably.
The installation of natural gas engines in decentralised CHP plants in Denmark has taken place since 1990. The first engines installed were re-latively small open-chamber engines and, in later years, mainly pre chamber engines were installed. As mentioned above, pre-chamber en-gines have a higher emission factor than open-chamber engines; there-fore, the emission factor has changed during the period 1990-2005. A time-series for the emission factor has been estimated and is presented below (Nielsen & Illerup 2003). The time-series was based on:
• Emission factors for different engine types • Data for year of installation for each engine and fuel consumption of
each engine 1994-2002 from the Danish Energy Authority (DEA 2003) • Research concerning the CH4 emission from gas engines carried out
in 1997 (Nielsen & Wit 1997)
371
�������� Time-series for the CH4 emission factor for natural gas fuelled engines.
The emission factor for biogas engines was estimated to be 323 g/GJ in 2000 and the same emission factor has been applied for 2001 - 2005. The emission factor for biogas engines was based on 18 emission measure-ments on 13 different plants. The plants from which emission measure-ments were available represented 18% of the total gas consumption in gas engines (year 2000).
The emission factor is lower than the factor for natural gas, mainly be-cause most engines are lean-burn open-chamber engines - not pre-chamber engines. A time-series for the emission factor has been esti-mated (Nielsen & Illerup 2003).
�������� Time-series for the CH4 emission factor for biogas fuelled engines.
The emission factor for gas turbines was estimated to be below 1.5g/GJ and the emission factor of 1.5 g/GJ has been applied for all years. The emission factor was based on emission measurements in 9 plants.
-:+3�5��!�*����+ + +%�� �!�+ + +,�� ��+ + +(�
The emission factor for CHP plants combusting wood was estimated to be below 2.1 g/GJ and the emission factor of 2 g/GJ has been applied for all years. The emission factor was based on emission measurements in 3 plants.
-:+3�����5�*����+ + +%�� ��+ + +,�
The emission factor for CHP plants combusting straw was estimated to be below 0.5g/GJ and the emission factor of 0.5g/GJ has been applied for all years. The emission factor was based on emission measurements in 4 plants.
The emission factor for CHP plants combusting municipal waste was es-timated to be below 0.59g/GJ and the emission factor of 0.59g/GJ has been applied for all years. The emission factor was based on emission measurements in 16 plants.
.� ����������������&��������������Emission factors for other plants refer to the EMEP/Corinair Guidebook (EMEP/Corinair 2004), the Danish Gas Technology Centre (DGC 2001) or Gruijthuijsen & Jensen 2000. The same emission factors are applied for 1990-2005.
�(�()� ��.�
The N2O emission factors applied for the 2005 inventory are listed in Ta-ble 3A-10. The same emission factors have been applied for 1990-2005.
Emission factors for gas engines, gas turbines and CHP plants combust-ing wood, straw or municipal waste all refer to emission measurements carried out on Danish plants (Nielsen & Illerup 2003). Emission factor for coal-powered plants in the public power sector refers to research con-ducted by DONG Energy (Previously Elsam). Other emission factors re-fer to the EMEP/Corinair Guidebook (EMEP/Corinair 2004).
• Danish research reports including: • An emission measurement program for decentralised CHP plants
(Nielsen & Illerup 2003) • Research and emission measurements programs for biomass fuels:
• Nikolaisen et al., 1998 • Jensen & Nielsen, 1990 • Dyrnum et al., 1990 • Hansen et al., 1994 • Serup et al., 1999
• Research and environmental data from the gas sector: • Gruijthuijsen & Jensen 2000 • Danish Gas Technology Centre 2001
• Calculations based on plant-specific emissions from a considerable number of power plants (Nielsen 2003).
• Calculations based on plant-specific emission data from a consider-able number of municipal waste incineration plants. These data refer to annual environmental reports published by plant operators.
• Sulphur content data from oil companies and the Danish gas trans-mission company.
• Additional personal communication. Emission factor time-series have been estimated for a considerable num-ber of the emission factors. These are provided in Appendix 3A-4.
The national statistics on which the emission inventories are based do not include a direct disaggregation to specific industrial subsectors. Ho-wever, separate national statistics from Statistics Denmark include a dis-aggregation to industrial subsectors. This part of the energy statistics is also included in the official energy statistics from the Danish Energy Au-thority.
Every other year, Statistics Denmark collects fuel consumption data for all industrial companies of considerable size. The deviation between the total fuel consumption from the Danish Energy Authority and the data collected by Statistics Denmark is rather small. Thus, the disaggregation to industrial subsectors available from Statistics Denmark can be applied for estimating disaggregation keys for fuel consumption and emissions.
The industrial fuel consumption is considered in respect of three aspects:
375
• Fuel consumption for transport. This part of the fuel consumption is not disaggregated to subsectors.
• Fuel consumption applied in power or district heating plants. Disag-gregation of fuel and emissions is plant specific.
• Fuel consumption for other purposes. The total fuel consumption and the total emissions are disaggregated to subsectors.
All pollutants included in the Climate Convention reporting have been disaggregated to industrial subsectors.
376
)� 8���������������!����
In 2005, total fuel consumption for stationary combustion plants was 531 PJ, of which 424 PJ related to fossil fuels. The fuel consumption rates are shown in Appendix 3A-3.
Fuel consumption distributed on the stationary combustion subsectors is shown in Figure 3A-1 and Figure 3A-2. The majority (57%) of all fuels is combusted in the sector, �����"� ���"���"���� � �������#���"�� 0 Other sec-tors with high fuel consumption are ������ ���� and $ ������. The energy consumption in category 1A1c is mainly natural gas used in gas turbines in the offshore industry.
Fuel consumption including renewable fuels
1A1a Public electricity and heat production57%
1A1b Petroleum refining3%
1A1c Other energy industries5%
1A2f Industry14%
1A4a Commercial / Institutional3%
1A4b Residential16%
1A4c Agriculture / Forestry / Fisheries2%
Fuel consumption, fossil fuels
1A4c Agriculture / Forestry / Fisheries2%
1A4b Residential13%
1A4a Commercial / Institutional3%
1A2f Industry16%
1A1c Other energy industries7%
1A1b Petroleum refining4%
1A1a Public electricity and heat production55%
��������� Fuel consumption rate of stationary combustion, 2005 (based on DEA 2006a).
Coal and natural gas are the most utilised fuels for stationary combus-tion plants. Coal is mainly used in power plants and natural gas is used
377
in power plants and decentralised CHP plants, as well as in industry, district heating and households.
��������� Fuel consumption of stationary combustion plants 2005 (based on DEA 2006a).
Fuel consumption time-series for stationary combustion plants are pre-sented in Figure 3A-3. The total fuel consumption has increased by 6.6% from 1990 to 2005, while the fossil fuel consumption has decreased by 4.9%. The consumption of natural gas and renewable fuels has increased since 1990, whereas coal consumption has decreased.
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Otherbiomass
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Other fossilfuels
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Natural gas
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��������� Fuel consumption time-series, stationary combustion (based on DEA 2006a).
The fluctuations in the time-series for fuel consumption are mainly a re-sult of electricity import/export, but also of outdoor temperature varia-tions from year to year. This, in turn, leads to fluctuations in emission le-vels. The fluctuations in electricity trade, fuel consumption and NOX
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378
emission are illustrated and compared in Figure 3A-4. In 1990 the Danish electricity import was large, causing relatively low fuel consumption, whereas the fuel consumption was high in 1996 due to a large electricity export. In 2005 the net electricity import was 4932 TJ in previous years there had been a net export. The electricity import in 2005 is a result of high rainfall in Norway and Sweden causing large hydropower produc-tion in both countries.
To be able to follow the national energy consumption, as well as for sta-tistical and reporting purposes, the Danish Energy Authority produces a correction of the actual fuel consumption without random variations in electricity imports/exports and ambient temperature. This fuel con-sumption trend is also illustrated in Figure 3A-4. The corrections are in-cluded here to explain the fluctuations in the emission time-series.
��������� Comparison of time-series fluctuations for electricity trade, fuel consumption and NOX emission (DEA 2006b).
Degree days Fuel consumption adjusted for electricity trade
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J]Otherbiomass
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Electricity trade Fluctuations in electricity trade compared to fuel consumption
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-150
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379
%� *���� �����"�����������
The total Danish greenhouse gas (GHG) emission in the year 2005 was 63 947 Gg CO2 equivalents, not including land-use change and forestry, or 62.494 Gg CO2 equivalents including land-use change and forestry. The greenhouse gas pollutants HFCs, PFCs and SF6 are not emitted from combustion plants and, as such, only the pollutants CO2, CH4 and N2O are considered below.
The global warming potentials of CH4 and N2O applied in greenhouse gas inventories refer to the second IPCC assessment report (IPCC 1995):
1 g CH4 equals 21 g CO2
1 g N2O equals 310 g CO2
The GHG emissions from stationary combustion are listed in Table 3A-11. The emission from stationary combustion accounts for 52% of the to-tal Danish GHG emission.
The CO2 emission from stationary combustion plants accounts for 64% of the total Danish CO2 emission (not including land-use change and for-estry). CH4 accounts for 9% of the total Danish CH4 emission and N2O for only 4% of the total Danish N2O emission.
��������� Greenhouse gas emission for the year 2005 1).
CO2 CH4 N2O
Gg CO2 equivalent
1A1 Fuel consumption, Energy industries 22130 292 142
1A2 Fuel consumption, Manufacturing Industries and Construction1)
4621 27 43
1A4 Fuel consumption, Other sectors 1) 5306 196 77
Total emission from stationary combustion plants 32058 515 262
Total Danish emission (gross) 50426 5636 7044
%
Emission share for stationary combustion 64 9 4
1) Only stationary combustion sources of the sector is included
CO2 is the most important GHG pollutant and accounts for 97.7% of the GHG emission (CO2 eq.). This is a much higher share than for the total Danish GHG emissions where CO2 only accounts for 81% of the GHG emission (CO2 eq.).
380
��������� GHG emission (CO2 equivalent), contribution from each pollutant.
Figure 3A-6 depicts the time-series of GHG emission (CO2 eq.) from sta-tionary combustion and it can be seen that the GHG emission develop-ment follows the CO2 emission development very closely. Both the CO2 and the total GHG emission are lower in 2005 than in 1990, CO2 by 15% and GHG by 14%. However, fluctuations in the GHG emission level are large.
�������� GHG emission time-series for stationary combustion.
The fluctuations in the time-series are mainly a result of electricity im-port/export activity, but also of outdoor temperature variations from year to year. The fluctuations follow the fluctuations in fuel consumption discussed in Chapter 3.
Figure 3A-7 shows the corresponding time-series for degree days, elec-tricity trade and CO2 emission. As mentioned in Chapter 3, the Danish Energy Authority estimates a correction of the actual emissions without random variations in electricity imports/exports and in ambient tem-perature. This emission trend, which is smoothly decreasing, is also illus-trated in Figure 3A-7. The corrections are included here to explain the fluctuations in the emission time-series. The GHG emission corrected for electricity import/export and ambient temperature has decreased by 23% since 1990, and the CO2 emission by 25%.
Stationary combustion Total Danish emission
CH4
1,2%N2O1,0%
CO2
97,7%
N2O11,0%
CH4
8,0%
CO2
81,0%
0
10
20
30
40
50
60
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
GH
G [
Tg
CO
2 eq
.]
Total
CO2
CH4N2O
381
��������� GHG emission time-series for stationary combustion, adjusted for electricity import/export (DEA 2006b).
%(�� -.��
The CO2 emission from stationary combustion plants is one of the most important GHG emission sources. Thus the CO2 emission from station-ary combustion plants accounts for 64% of the total Danish CO2 emis-sion. Table 3A-12 lists the CO2 emission inventory for stationary combus-tion plants for 2005. Figure 3A-8 reveals that ���"���"����� �������#���"�� accounts for 61% of the CO2 emission from stationary combustion. This share is somewhat higher than the fossil fuel consumption share for this sector, which is 55% (Figure 3A-1). Other large CO2 emission sources are industrial plants and residential plants. These are the sectors which also account for a considerable share of fuel consumption.
��������� CO2 emission from stationary combustion plants 2005 1)
1) Only emission from stationary combustion plants in the sectors is included
Fluctuations in electricity trade compared to fuel consumption CO2 emission adjustment as a result of electricity trade
1A1a Public electricity and heat production 19606 Gg
1A1b Petroleum refining 932 Gg
1A1c Other energy industries 1593 Gg
1A2 Industry 4621 Gg
1A4a Commercial / Institutional 911 Gg
1A4b Residential 3712 Gg
1A4c Agriculture / Forestry / Fisheries 683 Gg
Total 32058 Gg
382
��������� CO2 emission sources, stationary combustion plants, 2005.
The sector ���"���"����� �������#���"�� consists of the SNAP source sec-tors: �����"� #�� and 1�����"�� ����� �. The CO2 emissions from each of these subsectors are listed in Table 3A-13. The most important subsector is power plant boilers >300MW.
��������� CO2 emission from subsectors to ���������������� �������� �����.
CO2 emission from combustion of biomass fuels is not included in the to-tal CO2 emission data, because biomass fuels are considered CO2 neutral. The CO2 emission from biomass combustion is reported as a memo item in Climate Convention reporting. In 2005, the CO2 emission from bio-mass combustion was 10615 Gg.
In Figure 3A-9 the fuel consumption share (fossil fuels) is compared with the CO2 emission share disaggregated to fuel origin. Due to the higher CO2 emission factor for coal than oil and gas, the CO2 emission share from coal combustion is higher than the fuel consumption share. Coal accounts for 35% of the fossil fuel consumption and for 45% of the CO2 emission. Natural gas accounts for 44% of the fossil fuel consumption but only 34% of the CO2 emission.
Time-series for the CO2 emission are provided in Figure 3A-10. Despite an increase in fuel consumption of 6.6% since 1990, the CO2 emission from stationary combustion has decreased by 15% because of the change of fuel type used.
The fluctuations in total CO2 emission follow the fluctuations in CO2 emission from ���"���"���� � �� ����� #���"�� (Figure 3A-10) and in coal consumption (Figure 3A-11). The fluctuations are the result of electricity import/export activity as discussed in Chapter 3.
Figure 3A-11 compares the time-series for fossil fuel consumption and the CO2 emission. As mentioned above, the consumption of coal has de-creased whereas the consumption of natural gas, with a lower CO2 emis-sion factor, has increased. Total fossil fuel use decreased by 4.9% be-tween 1990 and 2005.
384
���������� CO2 emission time-series for stationary combustion plants.
Fuel consumption
0
100
200
300
400
500
600
700
800
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
Fue
l con
sum
ptio
n [P
J]
REFINERY GAS
LPG
NATURAL GAS
ORIMULSION
KEROSENE
GAS OIL
RESIDUAL OIL
PLASTIC WASTE
PETROLEUM COKE
COKE OVEN COKE
BROWN COAL BRI.
COAL
CO2 emission, fuel origin
0
10
20
30
40
50
60
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
CO
2 em
issi
on [T
g]
REFINERY GAS
LPG
NATURAL GAS
ORIMULSION
KEROSENE
GAS OIL
RESIDUAL OIL
PLASTIC WASTE
PETROLEUM COKE
COKE OVEN COKE
BROWN COAL BRI.
COAL
���������� Fossil fuel consumption and CO2 emission time-series for stationary com-bustion.
0
10
20
30
40
50
60
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
CO
2 [T
g]
1A1a Publicelectricity and heatproduction1A1b Petroleumrefining
1A1c Other energyindustries
1A2 Industry
1A4a Commercial /Institutional
1A4b Residential
1A4c Agriculture /Forestry / Fisheries
Total
Total
385
%(�� -:��
CH4 emission from stationary combustion plants accounts for 9% of the total Danish CH4 emission. Table 3A-14 lists the CH4 emission inventory for stationary combustion plants in 2005. Figure 3A-12 reveals that ���"����"����� �������#���"�� accounts for 57% of the CH4 emission from sta-tionary combustion, this being closely aligned with fuel consumption share.
��������� CH4 emission from stationary combustion plants 2005 1).
CH4 2005
1A1a Public electricity and heat production 13842 Mg
1A1b Petroleum refining 2 Mg
1A1c Other energy industries 80 Mg
1A2 Industry 1280 Mg
1A4a Commercial / Institutional 834 Mg
1A4b Residential 6603 Mg
1A4c Agriculture / Forestry / Fisheries 1885 Mg
Total 24527 Mg
1) Only the emission from stationary combustion plants in the sectors is included
The CH4 emission factor for reciprocating gas engines is much higher than for other combustion plants due to the continuous ignition/burn-out of the gas. Lean-burn gas engines have an especially high emission factor, as discussed in Chapter 2.5.2. A considerable number of lean-burn gas engines are in operation in Denmark and these plants account for 67% of the CH4 emission from stationary combustion plants (Figure 3A-13). The engines are installed in CHP plants and the fuel used is either natural gas or biogas.
1A1b Petroleum refining0,01%
1A1c Other energy industries0,3%
1A2 Industry5%
1A4b Residential27%
1A4a Commercial / Institutional3%
1A4c Agriculture / Forestry / Fisheries8%
1A1a Public electricity and heat production57%
386
Gas engines67%
Other stationary combustion plants33%
���������� Gas engine CH4 emission share, 2005.
The CH4 emission from stationary combustion increased by a factor of 4.2 since 1990 (Figure 3A-14). This results from the considerable number of lean-burn gas engines installed in CHP plants in Denmark in this pe-riod. Figure 3A-15 provides time-series for the fuel consumption rate in gas engines and the corresponding increase in the CH4 emission.
���������� CH4 emission time-series for stationary combustion plants.
0
5
10
15
20
25
30
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
CH
4 [G
g]
1A1a Publicelectricity and heatproduction1A1b Petroleumrefining
1A1c Other energyindustries
1A2 Industry
1A4a Commercial /Institutional
1A4b Residential
1A4c Agriculture /Forestry / Fisheries
Total
Total
387
0
5
10
15
20
25
30
35
40
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
Fue
l con
sum
ptio
n [P
J]
Gas engines, Natural gas Gas engines, Biogas
0
5
10
15
20
25
30
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
CH
4 em
issi
on [G
g]
Gas engines Other stationary combustion plants
���������� Fuel consumption and CH4 emission from gas engines, time-series.
%()� ��.�
The N2O emission from stationary combustion plants accounts for 4% of the total Danish N2O emission. Table 3A-15 lists the N2O emission inven-tory for stationary combustion plants in the year 2005. Figure 3A-16 re-veals that ���"���"���� � �� ����� #���"�� accounts for 43% of the N2O emission from stationary combustion.
��������� N2O emission from stationary combustion plants 2005 1).
N2O 2005
1A1a Public electricity and heat production 364 Mg
1A1b Petroleum refining 33 Mg
1A1c Other energy industries 61 Mg
1A2 Industry 140 Mg
1A4a Commercial / Institutional 24 Mg
1A4b Residential 197 Mg
1A4c Agriculture / Forestry / Fisheries 26 Mg
Total 846 Mg
1) Only the emission from stationary combustion plants in the sectors is included
Figure 3A-17 shows time-series for N2O emission. The N2O emission from stationary combustion increased by 9% from 1990 to 2005, but again fluctuations in emission level due to electricity import/export are con-siderable.
���������� N2O emission time-series for stationary combustion plants.
1A1b Petroleum refining4%
1A1c Other energy industries7%
1A2 Industry17%
1A4b Residential23%
1A4a Commercial / Institutional3%
1A4c Agriculture / Forestry / Fisheries3%
1A1a Public electricity and heat production43%
�
0,0
0,2
0,4
0,6
0,8
1,0
1,2
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
N2O
[Gg]
1A1a Publicelectricity and heatproduction1A1b Petroleumrefining
1A1c Other energyindustries
1A2 Industry
1A4a Commercial /Institutional
1A4b Residential
1A4c Agriculture /Forestry / Fisheries
Total
Total
389
�� 0.�3��.=3��1;.-���!�-.�
The emissions of SO2, NOX, NMVOC and CO from Danish stationary combustion plants 2005 are presented in Table 3A-16. The emission of these pollutants are also included in the report to the Climate Conven-tion.
SO2 from stationary combustion plants accounts for 84% of the total Dan-ish emission. NOX, CO and NMVOC account for 37%, 45% and 20% of total Danish emissions, respectively.
�������� SO2, NOX, NMVOC and CO emission from stationary combustion 2005 1).
1) Only emissions from stationary combustion plants in the sectors are included
�(�� 0.��
Stationary combustion is the most important emission source for SO2, ac-counting for 84% of the total Danish emission. Table 3A-17 and Figure 3A-18 present the SO2 emission inventory for the stationary combustion subsectors.
���"���"����� �������#���"�� �is the largest emission source accounting for 42% of the emission. However, the SO2 emission share is lower than the fuel consumption share for this sector, which is 57%. This is possibly due to effective flue gas desulphurisation equipment installed in power plants combusting coal. Figure 3A-19 shows the SO2 emission from ���"����"���� � �� ����� #���"�� on a disaggregated level. Power plants >300MWth represent the main emission source, accounting for 66% of the emission.
The fuel origin of the SO2 emission is shown in Figure 3A-20. Disaggre-gation of total emissions from point sources using several fuels is based on emission factors. As expected, the emission from natural gas is negli-gible and the emission from coal combustion is considerable (49%). Most remarkable is the emission share from residual oil combustion, which is 26%. This emission is very high compared with the fuel consumption share of 4%. The emission factor for residual oil combusted in the indus-
Pollutant NOX
Gg
CO
Gg
NMVOC
Gg
SO2
Gg
1A1 Fuel consumption, Energy industries 47.9 11.2 3.8 8.1
1A2 Fuel consumption, Manufacturing Industries and Construction (Stationary combustion) 12.5 12.4 0.6 6.0
Total emission from stationary combustion plants 68.5 274.0 23.6 18.3
Total Danish emission 185.8 611.2 118.3 21.9
%
Emission share for stationary combustion 37 45 20 84
390
trial sector is uncertain because knowledge of the applied flue gas clean-ing technology in this sector is insufficient.
The SO2 emission from $ ������ is 33%, a remarkably high emission sha-re compared with fuel consumption. The main emission sources in the industrial sector are combustion of coal and residual oil, but emissions from the cement industry also represent a considerable emission source. Some years ago, the SO2 emission from the industrial sector only ac-counted for a small portion of the total emission, but as a result of re-duced emissions from power plants the share has now increased.
Time-series for SO2 emission from stationary combustion are shown in Figure 3A-21. The SO2 emission from stationary combustion plants has decreased by 96% from 1980 and 85% from 1995. The large emission de-crease is mainly a result of the reduced emission from ���"���"����� �������#���"�� , made possible due to installation of desulphurisation plants and due to the use of fuels with lower sulphur content. Despite the con-siderable reduction in emission from electricity and heat production plants, these still account for 42% of the total emission from stationary combustion, as mentioned above. The emission from other sectors also decreased considerably since 1980.
��������� SO2 emission from stationary combustion plants 2005 1).
SO2 2005
ublic electricity and heat production 7716 Mg
1A1b Petroleum refining 325 Mg
1A1c Other energy industries 10 Mg
1A2 Industry 6045 Mg
1A4a Commercial / Institutional 260 Mg
1A4b Residential 2381 Mg
1A4c Agriculture / Forestry / Fisheries 1612 Mg
Total 18350 Mg
1) Only emission from stationary combustion plants in the sectors is included
���������� Disaggregated SO2 emissions from ������������������ �����
���������� Fuel origin of the SO2 emission from stationary combustion plants.
���������� SO2 emission time-series for stationary combustion.
�(�� �.��
Stationary combustion accounts for 37% of the total Danish NOX emis-sion. Table 3A-18 and Figure 3A-22 show the NOX emission inventory for stationary combustion subsectors.
���"���"����� �������#���"�� �is the largest emission source accounting for 58% of the emission from stationary combustion plants.
District heating, stationary engines0%
Public power, gas turbines5%
District heating, boilers > 50MW0,5%
Public power, stationary engines0,5%
District heating, boilers < 50MW9%
Public power, boilers < 50MW3%
District heating, gas turbines0%
Public power, boilers between 50MW and 300MW16%
Public power, boilers > 300MW66%
Fuel consumption SO2 emission, fuel origin Biogas, fish &
rape oil1%
Coal & coke29%
Petroleum Coke2%
Wood9%
Municipal waste
7%Straw
3%
Natural gas, LPG, refinery gas, kerosene
39%
Gas oil6%
Residual oil4%
Coal & coke49%
Gas oil4%
, ygas, kerosene0,47% Biogas, fish &
rape oil0,0%
Residual oil26%
Straw8%
Municipal waste3% Wood
5%Petroleum coke5%
0
50
100
150
200
250
300
350
400
450
1980
1982
1984
1986
1988
1990
1992
1994
1996
1998
2000
2002
2004
SO
2 [G
g]
1A1a Publicelectricity and heatproduction1A1b Petroleumrefining
1A1c Other energyindustries
1A2 Industry
1A4a Commercial /Institutional
1A4b Residential
1A4c Agriculture /Forestry / Fisheries
Total
Total
392
Figure 3A-23 shows fuel origin of the NOX emission from sector 1A1a Electricity and heat production. The fuel origin of the NOX emission is almost the same as the fuel consumption in this plant category. The emission from coal combustion is, however, somewhat higher than the fuel consumption share.
Industrial combustion plants are also an important emission source, ac-counting for 18% of the emission. The main industrial emission source is cement production, accounting for 67% of the emission.
Residential plants accounts for 8% of the NOX emission. The fuel origin of this emission is mainly wood, gas oil and natural gas, accounting for 51%, 22% and 20% of the residential plant emission, respectively.
Time-series for NOX emission from stationary combustion are shown in Figure 3A-24. The NOX emission from stationary combustion plants has decreased by 54% from 1985 and 38% from 1995. The reduced emission is largely a result of the reduced emission from ���"���"����� �������#���"��� due to installation of low NOX burners and selective catalytic reduc-tion (SCR) units0 The fluctuations in the time-series follow the fluctua-tions in ���"���"����� �������#���"�� , which, in turn, result from electric-ity trade fluctuations.
��������� NOX emission from stationary combustion plants 2005 1).
2005
1A1a Public electricity and heat production 39367 Mg
1A1b Petroleum refining 1513 Mg
1A1c Other energy industries 6998 Mg
1A2 Industry 12482 Mg
1A4a Commercial / Institutional 1075 Mg
1A4b Residential 5762 Mg
1A4c Agriculture / Forestry / Fisheries 1309 Mg
Total 68506 Mg
1) Only the emission from stationary combustion plants in the sectors is included
����������� NOX emissions from 1A1a Electricity and heat production, fuel origin.
���������� NOX emission time-series for stationary combustion.
�()� �1;.-�
Stationary combustion plants account for 20% of the total Danish NMVOC emission. Table 3A-19 and Figure 3A-25 present the NMVOC emission inventory for the stationary combustion subsectors.
Residential plants are the largest emission source accounting for 72% of the total emission from stationary combustion plants. For residential plants, NMVOC is mainly emitted from wood and straw combustion, see Figure 3A-26. In 2006 the Danish Energy Authority revised the national statistics for wood consumption in the residential sector. This meant a significant increase from 2000 onwards.
Electricity and heat production is also a considerable emission source, accounting for 16% of the total emission. Lean-burn gas engines have a relatively high NMVOC emission factor and are the most important emission source in this subsector (see Figure 3A-26). The gas engines are either natural gas or biogas fuelled.
Time-series for NMVOC emission from stationary combustion are shown in Figure 3A-27. The emission has increased by 169% from 1985 and 137% from 1995. The increased emission is mainly a result of higher
Fuel consumption NOX emission, fuel origin
Coal47%
Biogas0,6%
LPG0%
Gas oil0,4%
Natural gas24,7%
Petroleum coke0%
Wood6%
Municipal waste12%
Straw5%
Residual oil4%
Coal55%
Residual oil4%
Straw5%
Municipal waste10%
Wood3%
Petroleum Coke0%
Fish & rape oil0,0%
Gas oil0,4%
Natural gas21%
Biogas2%LPG
0%
0
20
40
60
80
100
120
140
160
180
1985
1987
1989
1991
1993
1995
1997
1999
2001
2003
2005
1A1a Publicelectricity and heatproduction1A1b Petroleumrefining
1A1c Other energyindustries
1A2 Industry
1A4a Commercial /Institutional
1A4b Residential
1A4c Agriculture /Forestry / Fisheries
Total
Total
394
wood consumption in the residential sector as well as the increased use of lean-burn gas engines in CHP plants as discussed in Chapter 7.2.
The emission from residential plants is 79% higher in 2005 than in 1990, but the NMVOC emission from wood combustion increased by 138% since 1990 due to increased wood consumption. However the emission from straw combustion in farmhouse boilers has decreased over this pe-riod.
The use of wood in residential boilers and stoves is relatively low in 1998-99 resulting in a lower emission level these years.
��������� NMVOC emission from stationary combustion plants 2005 1).
2005
1A1a Public electricity and heat production 3709 Mg
1A1b Petroleum refining 2 Mg
1A1c Other energy industries 42 Mg
1A2 Industry 600 Mg
1A4a Commercial / Institutional 569 Mg
1A4b Residential 17125 Mg
1A4c Agriculture / Forestry / Fisheries 1567 Mg
Total 23614 Mg
1) Only the emission from stationary combustion plants in the sectors is included
��������� NMVOC emission from residential plants and from electricity and heat production, 2005.
0
5
10
15
20
25
1985
1987
1989
1991
1993
1995
1997
1999
2001
2003
2005
NM
VO
C [G
g]
1A1a Publicelectricity and heatproduction1A1b Petroleumrefining
1A1c Other energyindustries
1A2 Industry
1A4a Commercial /Institutional
1A4b Residential
1A4c Agriculture /Forestry / Fisheries
Total
Total
���������� NMVOC emission time-series for stationary combustion.
�(%� -.�
Stationary combustion accounts for 45% of the total Danish CO emission. Table 3A-20 and Figure 3A-28 presents the CO emission inventory for stationary combustion subsectors.
Residential plants are the largest emission source, accounting for 88% of the emission. Wood combustion accounts for 94% of the emission from residential plants, see Figure 3A-29. This is in spite of the fact that the fuel consumption share is only 30%. Combustion of straw is also a con-siderable emission source, whereas the emission from other fuels used in residential plants is almost negligible.
Time-series for CO emission from stationary combustion are shown in Figure 3A-30. The emission has increased by 152% from 1985 and in-creased 145% from 1995. The time-series for CO from stationary combus-tion plants follows the time-series for CO emission from residential plants.
The consumption of wood in residential plants has increased by 180% since 1990 leading to an increase in the CO emission. The increase in the CO emission from residential plants is lower than the increase in wood
Residential plants Electricity and heat production
Other plants
23%
Gas
engines
77%
Natural gas1,7% Other
0,4%Straw 10,2%
Wood 87,7%
396
consumption, because CO emission from straw-fired farmhouse boilers has decreased considerably. Both the annual straw consumption in resi-dential plants and the CO emission factor for farmhouse boilers have de-creased.
���������� CO emission from stationary combustion plants 2005 1).
2005
1A1a Public electricity and heat production 10789 Mg
1A1b Petroleum refining 223 Mg
1A1c Other energy industries 209 Mg
1A2 Industry 12373 Mg
1A4a Commercial / Institutional 952 Mg
1A4b Residential 240970 Mg
1A4c Agriculture / Forestry / Fisheries 8494 Mg
Total 274010 Mg
1) Only the emission from stationary combustion plants in the sectors is included
���������� CO emission sources, stationary combustion plants, 2005.
���������� CO emission sources, residential plants, 2005.
1A1b Petroleum refining0%
1A1c Other energy industries0%
1A2 Industry5%
1A4b Residential88%
1A4a Commercial / Institutional0%
1A4c Agriculture / Forestry / Fisheries3%
1A1a Public electricity and heat production4%
Wood
94%
Other fuels 1%
Straw 5%
397
0
20
40
60
80
100
120
140
160
180
200
220
240
260
280
1985
1987
1989
1991
1993
1995
1997
1999
2001
2003
2005
CO
[Gg]
1A1a Publicelectricity and heatproduction1A1b Petroleumrefining
1A1c Other energyindustries
1A2 Industry
1A4a Commercial /Institutional
1A4b Residential
1A4c Agriculture /Forestry / Fisheries
Total
Total
���������� CO emission time-series for stationary combustion.
398
<� >�?>-���!�$���!������
The elaboration of a formal QA/QC plan started in 2004. A first version is now available, Sørensen et al., 2005.
The quality manual describes the concepts of quality work and defini-tions of sufficient quality, critical control points and a list of Point for Measuring (PM). Please see the general chapter on QA/QC.
The work on expanding the QC will be ongoing in future years.
Data storage level 1
��������� List of external data sources
Data Storage
level 1
1. Accuracy DS.1.1.1 General level of uncertainty for every dataset including the reasoning for the specific values
Since the DEA are responsible for the official Danish energy statistics as well as reporting to the IEA, NERI regards the data as being complete and in accordance with the official Danish energy statistics and IEA re-
Energiproducenttællingen.xls Data set for all electricity and heat producing plants.
Activity data The Danish Energy Authority (DEA)
Peter Dal Data agreement in place
Gas consumption for gas engines and gas turbines 1990-1994
Activity data DEA Peter Dal No data agreement. Historical data
Basic data (Grunddata.xls) Data set used for IPCC reference approach
Activity data DEA Peter Dal Not necessary. Pub-lished as part of na-tional energy statistics
Energy statistics The Danish energy statistics on SNAP level
Activity data DEA Peter Dal Data agreement in place
SO2 & NOx data, plants>25 MWe Emissions DEA Marianne Nielsen No data agreement in place
Emission factors Emission factors stems from a large number of sources
Emission factors
See chapter re-garding emission factors
HM and PM from public power plants
Emissions from the two large power plant operator in DK Elsam & E2
Emissions Elsam
Energi E2
Helle M. Iversen & Egon Raun Han-sen.
Helle Herk-Hansen & Henrik Lous
No formal data agreement in place
Environmental reports Emissions from plants defined as large point sources
Emissions Various plants No data agreement necessary.
Plants are obligated by law.
Additional data Fuel consumption and emissions from large industrial plants
AD & emis-sions
Aalborg Portland
Statoil
Shell
Henrik M. Thom-sen
Peder Nielsen
Lis R. Rasmussen
No formal data agreement in place
399
porting. The uncertainties connected with estimating fuel consumption do not, therefore, influence the accordance between IEA data, the energy statistics and the dataset on SNAP level utilised by NERI. For the re-mainder of the datasets, it is assumed that the level of uncertainty is rela-tively low. For further comments regarding uncertainties, see Chapter 7.
Data Storage
level 1
1. Accuracy DS.1.1.2 Quantification of the uncertainty level of every single data value including the rea-soning for the specific values.
The uncertainty for external data is not quantified. The uncertainties of activity data and emission factors are quantified see Chapter 7.
Data Storage
level 1
2.Comparability DS.1.2.1 Comparability of the data values with simi-lar data from other countries, which are comparable with Denmark, and evaluation of discrepancy.
On the external data the comparability has not been checked. However, at CRF level a project has been carried out comparing the Danish inven-tories with those of other countries.
Data Storage
level 1
3.Completeness DS.1.3.1 Documentation showing that all possible national data sources are included by setting up the reasoning for the selection of datasets.
See the above table for an overview of external datasets.
Danish Energy Authority
0��������������������������������!�������� �����"���!���5����������A spreadsheet from DEA listing fuel consumption of all plants included as large point sources in the emission inventory. The statistic on fuel consumption from district heating and power plants is regarded as com-plete and with no significant uncertainty since the plants are bound by law to report their fuel consumption and other information.
*������������������"�����"�������!�"������&���������,���%�For the years 1990-1994 DEA has estimated consumption of natural gas and biogas in gas engines and gas turbines. NERI assesses that the esti-mation by the DEA are the best available data.
4�����!����A spreadsheet from DEA used for the CO2 emission calculation in accor-dance with the IPCC reference approach. It is published annually on DEA’s webpage; therefore, a formal data delivery agreement is not dee-med necessary.
/���"����������������0��+���$����The DEA reports fuel consumption statistics on SNAP level based on a correspondence table developed in co-operation with NERI. Both traded and non-traded fuels are included in the Danish energy statistics. Thus,
400
for example, estimation of the annual consumption of non-traded wood is included. Petroleum coke, purchased abroad and combusted in Danish residential plants (border trade), is added to the apparent consumption of petroleum coke and the emissions are included in the inventory.
Emissions from non-energy use of fuels have not been included in the Danish inventory, to date, but the non-energy use of fuels is, however, included in the reference approach for Climate Convention reporting.
0.����!��.����������!�����������������������!����"��������@���17��Plants larger than 25 MWe are obligated to report emission data for SO2 and NOx to the DEA annually. Data is on block level and are classified. The data on plant level are part of the plants annually environmental re-ports. NERI’s QC of the data consists of a comparison with data from previous years and with data from the plants’ annual environmental re-ports.
/���������������������5�!�����"�������������For specific references, see the chapter regarding emission factors.
�����!�/���"��/��The two major Danish power plant operators assess heavy metal emis-sions from their plants using model calculations based on fuel data and type of flue gas cleaning. NERI’s QC of the data consists of a comparison with data from previous years and with data from the plants’ annual en-vironmental reports.
���������$�����������������������������!�����!�������"����������������A large number of plants are obligated by law to publish an environ-mental report annually with information on, among other things, emis-sions. NERI compares data with those from previous years large dis-crepancies are checked.
0����������"�!�����������"����!����������&��������������Fuel consumption and emissions from a few large industrial combustion plants are obtained directly from the plants. NERI compares the data with those from previous years and large discrepancies are checked.
Data Storage
level 1
4.Consistency DS.1.4.1 The origin of external data has to be pre-served whenever possible without explicit arguments (referring to other PM’s)
It is ensured that all external data are archived at NERI. Subsequent data processing takes place in other spreadsheets or databases. The datasets are archived annually in order to ensure that the basic data for a given report are always available in their original form.
Data Storage
level 1
6.Robustness DS.1.6.1 Explicit agreements between the external institution of data delivery and NERI about the condition of delivery
For stationary combustion a data delivery agreement is made with the DEA. Most of the other external data sources are available due to legisla-tory requirements. See table.
401
Data Storage
level 1
7.Transparency DS.1.7.1 Summary of each dataset including the reasoning for selecting the specific dataset
See DS 1.3.1
Data Storage
level 1
7.Transparency DS.1.7.3 References for citation for any external data set have to be available for any single num-ber in any dataset.
See table 3A-22 for general references. Much documentation already ex-ists. However, some of the information used is classified and therefore not publicly available.
Data Storage
level 1
7.Transparency DS.1.7.4 Listing of external contacts for every dataset
See Table 3A-22.
1������"���� �������� �
Data Processing
level 1
1. Accuracy DP.1.1.1 Uncertainty assessment for every data source as input to Data Storage level 2 in relation to type of variability. (Distribution as: normal, log normal or other type of variabil-ity)
The uncertainty assessment of activity data and emission factors and dis-cussed in the chapter concerning uncertainties.
Data Processing
level 1
1. Accuracy DP.1.1.2 Uncertainty assessment for every data source as input to Data Storage level 2 in relation to scale of variability (size of varia-tion intervals)
The uncertainty assessment of activity data and emission factors are dis-cussed in the chapter concerning uncertainties.
Data Processing
level 1
1. Accuracy DP.1.1.3 Evaluation of the methodological approach using international guidelines
The methodological approach is consistent with international guidelines.
Data Processing
level 1
1. Accuracy DP.1.1.4 Verification of calculation results using guideline values
Calculated emission factors are compared with guideline emission fac-tors to ensure that they are within reason.
402
Data Processing
level 1
2.Comparability DP.1.2.1 The inventory calculation has to follow the international guidelines suggested by UNFCCC and IPCC.
The calculations follow the principle in international guidelines.
Data Processing
level 1
3.Completeness DP.1.3.1 Assessment of the most important quantita-tive knowledge which is lacking.
Regarding the distribution of energy consumption for industrial sources, a more detailed and frequently updated data material would be pre-ferred. There is ongoing work to increase the accuracy and completeness of this sector. It is not assessed that this has any influence on the emis-sion of greenhouse gases.
Data Processing
level 1
3.Completeness DP.1.3.2 Assessment of the most important cases where accessibility to critical data sources that could improve quantitative knowledge is missing.
There is no missing accessibility to critical data sources.
Data Processing
level 1
4.Consistency DP.1.4.1 In order to keep consistency at a higher level, an explicit description of the activities needs to accompany any change in the calculation procedure.
A change in calculation procedure would entail that an updated descrip-tion would be elaborated.
Data Processing
level 1
5.Correctness DP.1.5.1 Show at least once, by independent calcula-tion, the correctness of every data manipula-tion.
During data processing it is checked that calculations are done correctly. However, documentation for this needs to be elaborated.
Data Processing
level 1
5.Correctness DP.1.5.2 Verification of calculation results using time-series
A time-series for activity data on SNAP level, as well as emission factors, is used to identify possible errors in the calculation procedure.
Data Processing
level 1
5.Correctness DP.1.5.3 Verification of calculation results using other measures
403
The IPCC reference approach validates the fuel consumption rates and CO2 emissions of fuel combustion. Fuel consumption rates and CO2 emissions differ by less than 1.6% (1990-2005). The reference approach is further discussed below.
Data Processing
level 1
5.Correctness DP.1.5.4 Show one-to-one correctness between external data sources and the databases at Data Storage level 2.
There is a direct line between the external datasets, the calculation proc-ess and the input data used to Data Storage level 2. During the calcula-tion process numerous controls are in place to ensure correctness, e.g. sum checks of the various stages in the calculation procedure.
Data Processing
level 1
7.Transparency DP.1.7.1 The calculation principle and equations used must be described.
Data Processing
level 1
7.Transparency DP.1.7.2 The theoretical reasoning for all methods must be described.
Data Processing
level 1
7.Transparency DP.1.7.3 Explicit listing of assumptions behind all methods
Where appropriate, this is included in the present report with annexes.
Data Processing
level 1
7.Transparency DP.1.7.4 Clear reference to dataset at Data Storage level 1
There is a clear line between the external data and the data processing.
Data Processing
level 1
7.Transparency DP.1.7.5 A manual log to collect information about recalculations
At present, a manual log table is not in place on this level. However, this feature will be implemented in the future. A manual log table is incorpo-rated in the national emission database, Data Storage level 2.
Data Storage level 2
5.Correctness DS.2.5.1 Documentation of a correct connection be-tween all data types at level 2 to data at level 1
To ensure a correct connection between data on level 2 to data on level 1, different controls are in place, e.g. control of sums and random tests.
Data Storage level 2
5.Correctness DS.2.5.2 Check if a correct data import to level 2 has been made.
404
Data import is checked by use of sum control and random testing. The same procedure is applied every year in order to minimise the risk of da-ta import errors.
.� ���>-������!�����The emission from each large point source is compared with the emis-sion reported the previous year.
Some automated checks have been prepared for the emission databases:
• Check of units for fuel rate, emission factors and plant-specific emis-sions
• Check of emission factors for large point sources. Emission factors for pollutants that are not plant-specific should be the same as those de-fined for area sources.
• Additional checks on database consistency • Most emission factor references are now incorporated in the emis-
sions database, itself. • Annual environmental reports are kept for subsequent control of
plant-specific emission data. • QC checks of the country-specific emission factors have not been per-
formed, but most factors are based on input from companies that have implemented some QA/QC work. The major power plant owner / operator in Denmark, DONG Energy, has obtained the ISO 14001 certification for an environmental management system. The Danish Gas Technology Centre and Force both run accredited labora-tories for emission measurements.
0�""����!�>�?>-�����������������������&�������The following points make up the list of QA/QC tasks to be carried out directly in relation to the stationary combustion part of the Danish emis-sion inventories. The time plan for the individual tasks has not yet been made.
1����������������� �
A fully comprehensive list of references for emission factors and activity data.
A comparison with external data from other countries in order to evalu-ate discrepancies.
1����#�"���� �������� �
Documentation list of model and independent calculations to test every single mathematical relation
<(�� ����������������� �
In addition to the sector-specific CO2 emission inventories (the national approach), the CO2 emission is also estimated using the reference ap-proach described in the IPCC Reference Manual (IPCC 1996). The refer-ence approach is based on data for fuel production, import, export and stock change. The CO2 emission inventory, based on the reference ap-
405
proach, is reported to the Climate Convention and used for verification of the official data in the national approach.
Data for import, export and stock change used in the reference approach originate from the annual “basic data” table prepared by the Danish En-ergy Authority and published on their homepage (DEA 2006b). The frac-tion of carbon oxidised has been assumed to be 1.00. Considerations re-garding this assumption are still ongoing.
The carbon emission factors are default factors originating from the IPCC Reference Manual (IPCC 1996). The country-specific emission factors are not used in the reference approach, this approach being for the purposes of verification.
The Climate Convention reporting tables include a comparison of the na-tional approach and the reference approach estimates. To make results comparable, the CO2 emission from incineration of the plastic content of municipal waste is added in the reference approach. Further consump-tion for non-energy purposes is subtracted in the reference approach, be-cause non-energy use of fuels is not, as yet, included in the Danish na-tional approach.
Three fuels are used for non-energy purposes: lube oil, bitumen and white spirit. The total consumption for non-energy purposes is relatively low – 12 PJ in 2005.
In 2005, the fuel consumption rates in the two approaches differ by -1.27% and the CO2 emission differs by -1.15%. In the period 1990-2005 fuel consumption and the CO2 emission differ by less than 1.6%. The dif-ferences are below 1% for all years except 1998 and 2005. According to IPCC Good Practice Guidance (IPCC 2000), the difference should be within 2%. The reference approach for 2005 and the comparison with the Danish national approach are provided in Appendix 3A-10. The appen-dix also includes a correspondence list for the fuel categories (Danish Energy Authority/IPCC reference approach).
A comparison of the national approach and the reference approach is il-lustrated in Figure 3A-32.
406
���������� Comparison of the reference approach and the national approach.
<(�� ��������������������
As part of the reporting for the Climate Convention a key source analysis for the Danish emission inventory has been performed. A key source has a significant influence on a country’s total inventory of greenhouse gases in terms of the absolute level of emission, the trend in emissions, or both.
Stationary combustion key sources for greenhouse gases are shown in Table 3A-23. The CO2 emission from eight different fuels is a key source in the Danish inventory. Furthermore, CH4 emission is a level and trend key source due to the increase in the production of electricity from gas engines.
The key source analysis will be considered in the future QC for station-ary combustion.
��������� Key sources, stationary combustion
-2,00
-1,50
-1,00
-0,50
0,00
0,50
1,00
1,50
2,00
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
%
Difference Energy consumption [%] Difference CO2 emission [%]
Source Pollutant Key source
Level or trend
CO2 Emission from Stationary Combustion Coal CO2 Yes Level, Trend
CO2 Emission from Stationary Combustion Petroleum coke CO2 Yes Level, Trend
CO2 Emission from Stationary Combustion Plastic waste CO2 Yes Level, Trend
CO2 Emission from Stationary Combustion Residual oil CO2 Yes Level, Trend
CO2 Emission from Stationary Combustion Gas oil CO2 Yes Level, Trend
CO2 Emission from Stationary Combustion Kerosene CO2 Yes Trend
CO2 Emission from Stationary Combustion Natural gas CO2 Yes Level, Trend
CO2 Emission from Stationary Combustion Refinery gas CO2 Yes Level
Non-CO2 Emission from Stationary Combustion CH4 Yes Level, Trend
407
'� A�����������
According to the IPCC Good Practice Guidance (IPCC 2000), uncertainty estimates should be included in the annual National Inventory Report.
Uncertainty estimates include uncertainty with regard to the total emis-sion inventory as well as uncertainty with regard to trends. The GHG emission from stationary combustion plants has been estimated with an uncertainty interval of ±8.4% and the decrease in the GHG emission since 1990 has been estimated to be 13.7% ± 2.2 %-age-points.
'(�� 1�� �!���"��
The Danish uncertainty estimates for GHGs are based on a methodology included in IPCC Good Practice Guidance (IPCC 2000). The estimates are based on uncertainties for emission factors and fuel consumption rates, respectively. The input data required for the uncertainty calculations are:
• Emission data for the base year and the last year
• Uncertainty for activity rates
• Uncertainty for emission factors
'(�(�� *���� �����"�����
The Danish uncertainty estimates for GHGs are based on the Tier 1 ap-proach in IPCC Good Practice Guidance (IPCC 2000). The uncertainty le-vels have been estimated for the following emission source subcategories within stationary combustion:
• CO2 emission from each of the fuel categories applied
• CH4 emission from gas engines
• CH4 emission from all other stationary combustion plants
• N2O emission from all stationary combustion plants
The separate uncertainty estimation for the CH4 emission from gas en-gines and CH4 emission from other plants does not follow the recom-mendations in the IPCC Good Practice Guidance. Disaggregation is ap-plied, because the CH4 emission from gas engines is much larger in Denmark than the emission from other stationary combustion plants, and the CH4 emission factor for gas engines is estimated with a much smaller uncertainty level than for other stationary combustion plants.
Most of the uncertainty estimates applied for activity rates and emission factors are default values from the IPCC Reference Manual. A few of the uncertainty estimates are, however, based on national estimates. A coun-try-specific uncertainty estimate will be estimated for future reporting.
408
��������� Uncertainty rates for activity rates and emission factors.
1) IPCC Good Practice Guidance (default value)
2) Kristensen (2001)
3) Jensen & Lindroth (2002)
4) NERI assumption
'(�(�� .� ��������������
With regard to other pollutants, IPCC methodologies for uncertainty es-timates have been adopted for the LRTAP Convention reporting activi-ties (Pulles & Aardenne 2003). The Danish uncertainty estimates are ba-sed on the simple Tier 1 approach.
The uncertainty estimates are based on emission data for the base year and year 2005 as well as on uncertainties for fuel consumption and emis-sion factors for each of the main SNAP sectors. The base year is 1990. The applied uncertainties for activity rates and emission factors are default values referring to Pulles & Aardenne 2003. The default uncertainties for emission factors are given in letter codes representing an uncertainty range. It has been assumed that the uncertainties were in the lower end of the range for all sources and pollutants. The applied uncertainties for emission factors are listed in Table 3A-25. The uncertainty for fuel con-sumption in stationary combustion plants was assumed to be 2%.
��������� Uncertainty rates for emission factors [%].
SNAP sector SO2 NOX NMVOC CO
01 10 20 50 20
02 20 50 50 50
03 10 20 50 20
'(�� ��������
The uncertainty estimates for stationary combustion emission invento-ries are shown in Table 3A-26. Detailed calculation sheets are provided in Appendix 3A-7.
IPCC Source category Gas Activity data un-certainty
%
Emission factor uncertainty
%
Stationary Combustion, Coal CO2 1 1) 5 3)
Stationary Combustion, BKB CO2 3 1) 5 1)
Stationary Combustion, Coke oven coke CO2 3 1) 5 1)
Stationary Combustion, Petroleum coke CO2 3 1) 5 1)
Stationary Combustion, Plastic waste CO2 5 4) 5 4)
Stationary Combustion, Residual oil CO2 2 1) 2 3)
Stationary Combustion, Gas oil CO2 4 1) 5 1)
Stationary Combustion, Kerosene CO2 4 1) 5 1)
Stationary Combustion, Orimulsion CO2 1 1) 2 3)
Stationary Combustion, Natural gas CO2 3 1) 1 3)
Stationary Combustion, LPG CO2 4 1) 5 1)
Stationary Combustion, Refinery gas CO2 3 1) 5 1)
Stationary Combustion Plants, gas engines CH4 2.2 1) 40 2)
Stationary Combustion Plants, other CH4 2.2 1) 100 1)
Stationary Combustion Plants N2O 2.2 1) 1000 1)
409
The uncertainty interval for GHG is estimated to be ±8.4% and the uncer-tainty for the trend in GHG emission is ±2.2%-age points. The main sour-ces of uncertainty for the GHG emission are N2O emission (all plants) and CO2 emission from coal combustion. The main source of uncertainty in the trend in the GHG emission is the N2O emission (all plants) and CO2 emission from the combustion of coal and natural gas.
The total emission uncertainty is 7% for SO2, 16% for NOX, 42% for NMVOC and 46% for CO.
Improvements and recalculations since the 2006 emission inventory in-clude:
• Update of fuel rates according to the latest energy statistics. The up-date included the years 1980-2004.
• Updated emission factor (NMVOC) for wood combustion in the resi-dential sector
• New data material has made it possible to update the disaggregation of sector 1A2 into subsectors. This has not influenced the total emis-sion from sector 1A2 only the distribution on sectors 1A2a-1A2f.
411
�� 8����������$������
Some planned improvements of the emission inventories are discussed below.
�C�����$�!�!������������������ �������������������The reporting of, and references for, the applied emission factors have been improved in the current year and will be further developed in fu-ture inventories.
�C�>�?>-���!�$���!������The work with implementing and expanding the QA/QC procedures will continue in future years.
)C�A��������������������Uncertainty estimates are largely based on default uncertainty levels for activity rates and emission factors. More country-specific uncertainty es-timates will be incorporated in future inventories.
412
��� -����������
The annual Danish emission inventories are prepared and reported by NERI. The inventories are based on the Danish energy statistics and on a set of emission factors for various sectors, technologies and fuels. Plant-specific emissions for large combustion sources are incorporated in the inventories.
Since 1990 fuel consumption has increased by 6.6% – fossil fuel con-sumption, however, has decreased by 4.9%. The use of coal has de-creased, whereas the use of natural gas and renewable fuels has in-creased. The Danish fuel consumption fluctuates due to variation in the import/export of electricity from year to year.
Stationary combustion plants account for 52% of the total Danish GHG emission. 64% of the Danish CO2 emission originates from stationary combustion plants, whereas stationary combustion plants account for 9% of the CH4 emission and 4% of the N2O emission.
Public power plants represent the most important stationary combustion emission source for CO2, N2O, SO2 and NOX.
Lean-burn gas engines installed in decentralised CHP plants are the largest stationary combustion emission source for CH4. Furthermore, these plants are also a considerable emission source for NMVOC.
Residential plants represent the most important stationary combustion source for CO and NMVOC. Wood combustion in residential plants is the predominant emission source.
The greenhouse gas emission (GHG) development follows the CO2 emis-sion development closely. Both the CO2 and the total GHG emission was lower in 2004 than in 1990, CO2 by 5% and GHG by 4%. However, fluc-tuations in the GHG emission level are large. The fluctuations in the time-series are a result of electricity import/export and of outdoor tem-perature variations from year to year.
The CH4 emission from stationary combustion has increased by a factor of 4.2 since 1990. This is a result of the considerable number of lean-burn gas engines installed in CHP plants in Denmark during this period.
The SO2 emission from stationary combustion plants has decreased by 85% since 1995. The considerable emission decrease is largely a result of the reduced emission from electricity and heat production due to instal-lation of desulphurisation technology and the use of fuels with lower sulphur content.
The NOX emission from stationary combustion plants has decreased by 38% since 1995. The reduced emission is mainly a result of the reduced emission from electricity and heat production. The fluctuations in the emission time-series follow fluctuations in electricity import/export.
413
The uncertainty level for the Danish greenhouse gas emission from sta-tionary combustion is estimated to be within a range of ±8.4% and the trend uncertainty within a range of ±2.2%-age points. The sources con-tributing the most to the uncertainty estimates are the N2O emission (all plants) and the CO2 emission from coal combustion.
414
�����������
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Bech, N. 1999: Personal communication, letter 05-11-1999, Sjællandske Kraftværker, SK Energi.
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415
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416
Nielsen, M. 2004: Energistyrelsen, personal communication, letter 28-06-2004.
Nielsen, M. & Illerup, J.B. 2003: Emissionsfaktorer og emissionsopgørelse for decentral kraftvarme. Eltra PSO projekt 3141. Kortlægning af emis-sioner fra decentrale kraftvarmeværker. Delrapport 6. Danmarks Mil-jøundersøgelser. 116 s. –Faglig rapport fra DMU nr. 442.(In Danish, with an English summary). Available at http://www.dmu.dk/1_viden-/2_Publikationer/3_fagrapporter/rapporter/FR442.pdf (06-07-2004).
Nielsen, M. & Wit, J. 1997: Emissionsforhold for gasdrevne kraftvar-meænlæg < 25MWe, Miljøstyrelsen, Arbejdsrapport Nr. 17 1997 (In Dan-ish).
Nikolaisen, L., Nielsen, C., Larsen, M.G., Nielsen, V. Zielke, U., Kris-tensen, J.K. & Holm-Christensen, B. 1998: Halm til energiformål, Teknik – Miljø – Økonomi, 2. udgave, 1998, Videncenter for halm og flisfyring (In Danish).
Pulles, T. & Aardenne,. J.v. 2001: Good Practice Guidance for LRTAP Emission Inventories, 7. November 2001. Available at http://reports.e-ea.eu.int/EMEPCORINAIR4/en/BGPG.pdf (06-07-2004).
Sander, B. 2002: Personal communication, e-mail 17-05-2002.
Serup, H., Falster, H., Gamborg, C., Gundersen, P., Hansen, L. Heding, N., Jacobsen, H.H., Kofman, P., Nikolaisen, L. & Thomsen, I.M. 1999: Træ til energiformål, Teknik – Miljø – Økonomi, 2. udgave, 1999, Videncenter for halm og flisfyring (In Danish).
Sørensen, P.B., Illerup, J.B., Nielsen, M., Lyck, E., Bruun, H.G., Winther, M., Mikkelsen, M.H., Gyldenkærne, S., 2005: Quality manual for the greenhouse gas inventory. Version 1. NERI, Denmark. Research notes from NERI no. 224. Available at http://www2.dmu.dk/1_viden/2-_Publikationer/3_arbrapporter/rapporter/AR224.pdf
417
�����!���
Appendix 3A-1: The Danish emission inventory for the year 2005 reported to the Climate Convention in 2007
Appendix 3A-2: IPCC/SNAP source correspondence list
Appendix 3A-3: Fuel rate
Appendix 3A-4: Emission factors
Appendix 3A-5: Large point sources
Appendix 3A-6: Uncertainty estimates
Appendix 3A-7: Lower Calorific Value (LCV) of fuels
Appendix 3A-8: Adjustment of CO2 emission
Appendix 3A-9: Reference approach
Appendix 3A-10: Emission inventory 2005 based on SNAP sectors
418
Appendix 3A-1 The Danish emission inventory for the year 2005 reported to the Climate Convention ��������� The Danish emission inventory for the year 2005 reported to the Climate Convention in 2007.
Greenhouse gas source and sink categories CO2 (1) CH4 N2O HFCs (2) PFCs (2) SF6
B. Chemical Industry 3,01 NA,NO NA,NO NA NA NA 3,01
C. Metal Production 15,58 NA,NO NA NA NA,NO NA,NO 15,58
D. Other Production NE NE
E. Production of Halocarbons and SF6 NA,NO NA,NO NA,NO NA,NO
F. Consumption of Halocarbons and SF6 (2) 805,14 13,90 21,75 840,80
G. Other NA NA NA NA NA NA NA
3. Solvent and Other Product Use 116,20 NA 116,20
4. Agriculture 3.646,20 6.234,07 9.880,27
A. Enteric Fermentation 2.630,04 2.630,04
B. Manure Management 1.016,17 557,38 1.573,55
C. Rice Cultivation NA,NO NA,NO
D. Agricultural Soils(3) NE,NO 5.676,68 5.676,68
E. Prescribed Burning of Savannas NA NA NA
F. Field Burning of Agricultural Residues NA,NO NA,NO NA,NO
G. Other NA NA NA
5. Land Use, Land-Use Change and Forestry(1) -1.452,76 -0,49 0,15 -1.453,11
A. Forest Land -1.823,36 NE,NO IE,NE,NO -1.823,36
B. Cropland 308,07 NA,NO NA,NO 308,07
C. Grassland 75,54 NO NO 75,54
D. Wetlands -13,01 -0,49 0,15 -13,36
E. Settlements NE,NO NE,NO NE,NO NE,NO
F. Other Land NE,NO NE,NO NE,NO NE,NO
G. Other������� NO NO NO NO
6. Waste 1,84 1.311,98 61,00 1.374,82
A. Solid Waste Disposal on Land NA,NE,NO 1.058,76 1.058,76
B. Waste-water Handling 253,22 60,99 314,20
C. Waste Incineration IE IE IE IE
D. Other 1,84 0,00 0,01 1,86
7. Other ����������������� �������� NA NA NA NA NA NA NA
Memo Items: (4)
International Bunkers 5.211,34 2,33 78,94 5.292,61
Aviation 2.575,38 1,03 27,52 2.603,93
Marine 2.635,96 1,30 51,42 2.688,68
419
Multilateral Operations NO NO NO NO
CO2 Emissions from Biomass 10.615,13 10.615,13
(1) For CO2 from Land Use, Land-use Change and Forestry the net emissions/removals are to be reported. For the purposes of reporting, the signs for removals are always negative (-) and for emissions positive (+).
(2) Actual emissions should be included in the national totals. If no actual emissions were reported, potential emissions should be included.
(3) Parties which previously reported CO2 from soils in the Agriculture sector should note this in the NIR.
(4) See footnote 8 to table Summary 1.A.
420
Appendix 3A-2 IPCC/SNAP source correspondence list ��������� Correspondence list for IPCC source categories 1A1, 1A2 and 1A4 and SNAP (EMEP/Corinair 2004).�
'()*�+� '()*����� ,)##��������
01 Combustion in energy and transformation industries
030312 Lime (includ. iron and steel and paper pulp industry)(f) 1A2f
030313 Asphalt concrete plants 1A2f
030314 Flat glass (f) 1A2f
030315 Container glass (f) 1A2f
030316 Glass wool (except binding) (f) 1A2f
030317 Other glass (f) 1A2f
030318 Mineral wool (except binding) 1A2f
030319 Bricks and tiles 1A2f
030320 Fine ceramic materials 1A2f
030321 Paper-mill industry (drying processes) 1A2d
030322 Alumina production 1A2b
030323 Magnesium production (dolomite treatment) 1A2b
030324 Nickel production (thermal process) 1A2b
030325 Enamel production 1A2f
030326 Other 1A2f
08 1) Other mobile sources and machinery
0804 1) Maritime activities
080403 1) National fishing 1A4c
0806 1) Agriculture 1A4c
0807 1) Forestry 1A4c
0808 1) Industry 1A2f
0809 1) Household and gardening 1A4b
1) Not stationary combustion. Included in a IPCC sector that also includes stationary combustion plants
2) Stoves, fireplaces and cooking is included in the sector 0202 or 020202 in the Danish inventory. It is not possible based on the Danish energy statistics to split the residential fuel consumption between stoves/fireplaces/cooking and residential boilers.
422
Appendix 3A-3 Fuel rate
��������� Fuel consumption rate of stationary combustion plants [TJ].
Gas engines: 010105, 010205, 010505, 030105, 020105, 020204, 020304
0.3 17 168 31 117 31 175 31
NATURAL GAS
1A1a, 1A2f
010103, 010202, 010203, 0301, 030103, 030106
0.3 17 42 36 2 14 28 4
NATURAL GAS
1A1c 010504 0.3 17 250 1, 8, 32 1.4 31 6.2 31
NATURAL GAS
1A4a, 1A4c
0201, 020103, 0203
0.3 17 30 1, 4, 11 2 14 28 4
NATURAL GAS
1A4b 0202, 020202 0.3 17 30 1, 4, 11 4 11 20 11
LPG 1A1a, 1A2f
010203, 0301 0.13 23 96 32 2 1 25 1
LPG 1A4a, 1A4c
0201, 0203 0.13 23 71 32 2 1 25 1
LPG 1A4b 0202 0.13 23 47 32 2 1 25 1
REFINERY GAS
1A1b 010304 1 2 170 9 1.4 35 6.2 35
BIOGAS 1A1a, 1A2f, 1A4a, 1A4c
010102, 010103, 010203, 0301, 0201, 020103, 0203
25 26 28 4 4 1 36 4
BIOGAS 1A1a, 1A1c, 1A2f, 1A4a, 1A4c
Gas engines: 010105, 010505, 030105, 020105, 020304
19.2 31 540 31 14 31 273 31
BIOGAS 1A2f 030102 25 26 59 4 4 1 36 4
437
1. Emission Inventory Guidebook 3rd edition, prepared by the UNECE/EMEP Task Force on Emissions Inventories and Projections, 2004 update. Available on the Internet at http://reports.eea.eu.int/EMEPCORINAIR4/en (11-04-2005)
2. NERI calculation based on plant specific data 1995-2002 3. Sander, B. 2002. Elsam, personal communication, e-mail 17-05-2002 4. Miljøstyrelsen, 2001. Luftvejledningen, Begrænsning af luftforurening fra virksomheder, Vejledning fra Miljøstyrelsen Nr. 2 2001
(Danish legislation) 5. Nikolaisen L., Nielsen C., Larsen M.G., Nielsen V. Zielke U., Kristensen J.K. & Holm-Christensen B. 1998 Halm til energiformål,
Teknik – Miljø – Økonomi, 2. udgave, 1998, Videncenter for halm og flisfyring (In Danish) 6. Jensen L. & Nielsen P.A. 1990. Emissioner fra halm- og flisfyr, dk-Teknik & Levnedsmiddelstyrelsen 1990 (In Danish) 7. Bjerrum M., 2002. Danish Technological Institute, personal communication 09-10-2002 8. Kristensen, P. (2004) Danish Gas Technology Centre, e-mail 31-03-2004 9. NERI calculation based on annual environmental reports of Danish plants year 2000 10. Risø National Laboratory home page - http://www.risoe.dk/sys/esy/emiss_e/emf25082000.xls 11. Gruijthuijsen L.v. & Jensen J.K., 2000. Energi- og miljøoversigt, Danish Gas Technology Centre 2000 (In Danish) 12. Dyrnum O., Warnøe K., Manscher O., Vikelsøe J., Grove A.., Hansen K.J., Nielsen P.A., Madsen H. 1990, Miljøprojekt 149/1990
Emissionsundersøgelse for pejse og brændeovne, Miljøstyrelsen (In Danish) 13. Hansen K.J., Vikelsøe J., Madsen H. 1994, Miljøprojekt 249/1994 Emissioner af dioxiner fra pejse og brændeovne, Miljøstyrelsen
(In Danish) 14. Danish Gas Technology Centre 2001, Naturgas – Energi og miljø (In Danish) 15. Same emission factors as for gas oil is assumed (NERI assumption) 16. Same emission factors as residual oil assumed (NERI assumption) 17. NERI calculation based on S content of natural gas 6mg(S)/mn
3 gas. The S content refers to the Danish natural gas transmission company Gastra (http://www.gastra.dk/dk/index.asp)
18. Estimated by NERI based on 2005 data reported by the plant owners to the electricity transmission companies and the Danish Energy Authority. NERI calculations are based on data forwarded by the Danish Energy Authority: Nielsen M. 2004. Energistyrel-sen, personal communication.
19. NERI calculation based on a sulphur content of 0,8% and a retention of sulphur in ash of 5%. The sulphur content has been assumed just below the limit value of 0,9% (reference no. 24)
20. NERI calculation based on a sulphur content of 1% (reference no. 24) and a retention of sulphur in ash of 5%. 21. Christiansen, B.H., Evald, A., Baadsgaard-Jensen, J. Bülow, K. 1997. Fyring med biomassebaserede restprodukter, Miljøprojekt
1999. Træ til energiformål, Teknik – Miljø – Økonomi, 2. udgave, 1999, Videncenter for halm og flisfyring (In Danish) 23. NERI calculation based on a sulphur content of 0,0003%. The approximate sulphur content is stated by Danish refineries. 24. Miljøstyrelsen, 2001.Bekendtgørelseom begrænsning af svovlindholdet i visse flydende og faste brændstoffer, Bekendtgørelse
532 af 25/05/2001 (Danish legislation) 25. NERI calculation based on a sulphur content of 0,7%. The sulhpur content refer to product data from Shell and Statoil available at
the internet at: http://www.statoil.dk/mar/svg01185.nsf/fs/erhverv-produkt (13-05-2004) 26. NERI calculation based on a H2S content of 200 ppm. The H2S content refer to Christiansen J. 2003, Personal communication�and
to Hjort-Gregersen K., 1999 Centralised Biogas Plants, Danish Institute of Agricultural and Fisheries Economics, 1999 27. NERI calculation based on a sulphur content of 0,05% S. The sulphur content refers to Bilag 750, Kom 97/0105
(http://www.folketinget.dk/?/samling/20041/MENU/00000002.htm) and to product sheets from Q8, Shell and Statoil 28. Miljøstyrelsen 1990. Bekendtgørelse om begrænsning af emissioner af svovldioxid, kvælstofoxider og støv fra store fyringsanlæg,
Bekendtgørelse 689 af 15/10/1990 (Danish legislation) 29. Same emission factor as for coal is assumed (NERI assumption) 30. Product sheet from Shell. Available on the internet at: http://www.shell.com/home/dk-
da/html/iwgen/app_profile/app_products_0310_1510.html (13-05-2004) 31. Nielsen, M. & Illerup, J.B: 2003. Emissionsfaktorer og emissionsopgørelse for decentral kraftvarme. Eltra PSO projekt 3141.
Kortlægning af emissioner fra decentrale kraftvarmeværker. Delrapport 6. Danmarks Miljøundersøgelser. 116 s. –Faglig rapport fra DMU nr. 442.(In Danish, whith an english summary). Available on the Internet at : http://www2.dmu.dk/1_viden/2_Publikationer/3_fagrapporter/rapporter/FR442.pdf
32. Revised 1996 IPCC Guidelines for National Greenhouse Gas Inventories: Reference Manual, 1996. Available on the Internet at http://www.ipcc-nggip.iges.or.jp/public/gl/invs6.htm (11-04-2005)
33. NERI calculation based on plant specific data 2003 34. NERI calculation based on plant specific data 2002 35. Same emission factor as for natural gas fuelled gas turbines is assumed 36. Wit, J. d & Andersen, S. D. 2003. Emissioner fra større gasfyrede kedler, Dansk Gasteknisk Center 2003. The emission factor
have been assumed to be the average value of the stated interval (NERI assumption). 37. Folkecenter for Vedvarende Energi, 2000. http://www.folkecenter.dk/plant-oil/emission/emission_rapsolie.pdf 38. Assumed same emission factor as for gas oil (NERI assumption). However the value is not correct – the emission factor 65 g/GJ
���������� Large point sources, plant-specific emissions (IPCC 1A1, 1A2 and 1A4)1).
LPS_id LPS name LPS part Sector (IPCC)
Sector (SNAP)
SO2 NOx NMVOC CO
001 Amagervaerket 01 1A1a 010101 x x
001 Amagervaerket 02 1A1a 010101 x x
001 Amagervaerket 03 1A1a 010101 x x
002 Svanemoellevaerket 05 1A1a 010101 x x
002 Svanemoellevaerket 07 1A1a 010104 x
003 H.C.Oerstedsvaerket 03 1A1a 010101 x x
003 H.C.Oerstedsvaerket 07 1A1a 010101 x x
003 H.C.Oerstedsvaerket 08 1A1a 010101 x
004 Kyndbyvaerket 21 1A1a 010101 x x
004 Kyndbyvaerket 22 1A1a 010101 x x
004 Kyndbyvaerket 26 1A1a 010101 x x
004 Kyndbyvaerket 28 1A1a 010101 x x
004 Kyndbyvaerket 51 1A1a 010104 x x
004 Kyndbyvaerket 52 1A1a 010104 x x
005 Masnedoevaerket 12 1A1a 010102 x
005 Masnedoevaerket 31 1A1a 010104 x x
007 Stigsnaesvaerket 01 1A1a 010101 x x
007 Stigsnaesvaerket 02 1A1a 010101 x x
007 Stigsnaesvaerket 03 1A1a 010101 x X
008 Asnaesvaerket 02 1A1a 010101 x x
008 Asnaesvaerket 03 1A1a 010101 x x
008 Asnaesvaerket 04 1A1a 010101 x x
008 Asnaesvaerket 05 1A1a 010101 x x
009 Statoil Raffinaderi 01 1A1b 010306 x
010 Avedoerevaerket 01 1A1a 010101 x x
010 Avedoerevaerket 02 1A1a 010104 x x
011 Fynsvaerket 03 1A1a 010101 x x
011 Fynsvaerket 07 1A1a 010101 x x
011 Fynsvaerket 08 1A1a 010102 x x x
012 Studstrupvaerket 03 1A1a 010101 x x
012 Studstrupvaerket 04 1A1a 010101 x x
014 Vendsysselvaerket 02 1A1a 010101 x x
014 Vendsysselvaerket 03 1A1a 010101 x x
017 Shell Raffinaderi 01 1A1b 010306 x x
017 Shell Raffinaderi 05 1A1b 010304 x x
018 Skaerbaekvaerket 01 1A1a 010101 x x
018 Skaerbaekvaerket 03 1A1a 010101 x x
019 Enstedvaerket 03 1A1a 010101 x x
019 Enstedvaerket 04 1A1a 010101 x x
446
020 Esbjergvaerket 03 1A1a 010101 x x
022 Oestkraft 05 1A1a 010102 x x
022 Oestkraft 06 1A1a 010102 x x
023 Danisco Ingredients 01 1A2f 030102 x
024 Dansk Naturgas Behan-dlingsanlaeg
01 1A1c 010502 x
025 Horsens Kraftvarmevaerk 01 1A1a 010102 x x x
025 Horsens Kraftvarmevaerk 02 1A1a 010104 x
026 Herningvaerket 01 1A1a 010102 x x x
027 Vestforbraendingen 01 1A1a 010102 x x
027 Vestforbraendingen 02 1A1a 010102 x x
028 Amagerforbraendingen 01 1A1a 010102 x x x x
029 Randersvaerket 01 1A1a 010102 x x
030 Grenaavaerket 01 1A1a 010102 x x x
031 Hilleroedvaerket 01 1A1a 010104 x
032 Helsingoervaerket 01 1A1a 010104 x
032 Helsingoervaerket 02 1A1a 010105 x
033 Staalvalsevaerket 01 1A2f 030102 x
034 Stora Dalum 01 1A2f 030102 x
035 Assens Sukkerfabrik 01 1A2f 030102 x
036 Kolding Kraftvarmevaerk 01 1A1a 010103 x x x
036 Kolding Kraftvarmevaerk 02 1A1a 010103 x x x
037 Maabjergvaerket 02 1A1a 010102 x x x
038 Soenderborg Kraftvarme-vaerk
01 1A1a 010102 x x x
038 Soenderborg Kraftvarme-vaerk
02 1A1a 010104 x
039 Kara Affaldsforbraending-sanlaeg
01 1A1a 010102 x x
040 Viborg Kraftvarmevaerk 01 1A1a 010104 x
042 Nordforbraendingen 01 1A1a 010102 x x
045 Aalborg Portland 01/03 1A2f 030311 x x x
046 Aarhus Nord 01 1A1a 010102 x
047 Reno Nord 01 1A1a 010103 x x
048 Silkeborg Kraftvarmevaerk 01 1A1a 010104 x
049 Rensningsanlægget Lynetten
01 1A4a 020103 x
050 I/S Fasan 01 1A1a 010203 x x x
051 AVV Forbrændingsanlæg 01 1A1a 010103 x x
053 Svendborg Kraftvarmeværk 01 1A1a 010102 x x x x
054 Kommunekemi 01 1A1a 010102 x x
054 Kommunekemi 02 1A1a 010102 x x
054 Kommunekemi 03 1A1a 010102 x x
447
056 Vestfyns Forbrænding 01 1A1a 010203 x x x
058 I/S Reno Syd 01 1A1a 010103 x x
059 I/S Kraftvarmeværk Thisted 01 1A1a 010103 x x
060 Knudmoseværket 01 1A1a 010103 x x
061 Kavo I/S Energien 01 1A1a 010103 x x x
062 VEGA (Vestforbraending Taastrup)
01 1A1a 010203 x x x
065 Haderslev Kraftvarmeværk 01 1A1a 010103 x x x
066 Frederiskhavn Affaldskraft-varmeværk
01 1A1a 010103 x x x
067 Vejen Kraftvarmeværk 01 1A1a 010103 x x x
068 Bofa I/S 01 1A1a 010203 x x
069 DTU 01 1A1a 010104 x
070 Næstved Kraftvarmeværk 01 1A1a 010104 x x
071 Maricogen 01 1A2f 030104 x
072 Hjørring KVV 01 1A1a 010104 x
075 Rockwool A/S Hedehusene 01 1A2f 030318 x x x
076 Rockwool A/S Vamdrup 01 1A2f 030318 x x x
077 Rockwool A/S Doense 01 1A2f 030318 x x x
078 Rexam Glass Holmegaard A/S
01 1A2f 030315 x x
085 L90 Affaldsforbrænding 01 1A1a 010102 x x x
086 Hammel Fjernvarme 01 1A1a 010203 x x x
Total 7944 31507 16 9570
1) Emission of the pollutants marked with “x” is plant specific. Emission of other pollutants is estimated based on emission factors. The total shown������������� only includes plant-specific data.
Liquid Primary Crude Oil TJ 796.527,52 116.941,73 585.939,63 -4.756,66 332.286,27 1,00 NCV 332.286,27Fossil Fuels Orimulsion TJ NA NA NA NA NA 1,00 NCV NA
Natural Gas Liquids TJ NA NA NA NA NA 1,00 NCV NASecondary Gasoline TJ 45.421,36 46.462,79 25,38 1.381,10 -2.447,91 1,00 NCV -2.447,91Fuels Jet Kerosene TJ 42.218,32 16.476,55 35.748,73 9.482,13 -19.489,09 1,00 NCV -19.489,09
Other Kerosene TJ NA NA NA NA NA 1,00 NCV NAShale Oil TJ NA NA NA NA 1,00 NCV NAGas / Diesel Oil TJ 89.897,26 41.335,79 13.917,43 12.616,46 22.027,58 1,00 NCV 22.027,58Residual Fuel Oil TJ 48.749,59 62.797,91 20.590,61 3.665,29 -38.304,21 1,00 NCV -38.304,21Liquefied Petroleum Gas (LPG) TJ 270,48 3.982,17 85,33 -3.797,02 1,00 NCV -3.797,02Ethane TJ NA NA NA NA 1,00 NCV NANaphtha TJ NA 126,65 10,15 -136,79 1,00 NCV -136,79Bitumen TJ 9.040,89 36,14 480,94 8.523,81 1,00 NCV 8.523,81Lubricants TJ 2.615,59 79,02 83,38 -65,91 2.519,09 1,00 NCV 2.519,09Petroleum Coke TJ 9.972,45 634,25 855,59 8.482,62 1,00 NCV 8.482,62Refinery Feedstocks TJ 2.743,82 5.700,92 37,23 -2.994,34 1,00 NCV -2.994,34Other Oil TJ NA NA NA NA 1,00 NCV NA
Other Liquid Fossil 736,02White Spirit NA 905,24 169,22 NA NA 736,02 1,00 NCV 736,02Liquid Fossil Totals 307.406,02Solid Primary Anthracite (2) TJ NA NA NA NA NA 1,00 NCV NAFossil Fuels Coking Coal TJ NA NA NA NA NA 1,00 NCV NA
Other Bituminous Coal TJ NA 148.049,41 2.342,52 NA -8.799,69 154.506,58 1,00 NCV 154.506,58Sub-bituminous Coal TJ NA NA NA NA NA NA 1,00 NCV NALignite TJ NA NA NA NA NA 1,00 NCV NAOil Shale TJ NA NA NA NA NA 1,00 NCV NAPeat TJ NA NA NA NA NA 1,00 NCV NA
Secondary BKB(3) and Patent Fuel TJ 0,02 -5,78 NA 5,80 1,00 NCV 5,80Fuels Coke Oven/Gas Coke TJ 1.049,91 NA 47,85 1.002,06 1,00 NCV 1.002,06
Other Solid Fossil 8.240,01Plastic part of municipal waste 8.240,01 NA NA NA NA 8.240,01 1,00 NCV 8.240,01Solid Fossil Totals 163.754,45Gaseous Fossil Natural Gas (Dry) TJ 392.868,34 NA 209.777,32 -1.103,34 184.194,36 1,00 NCV 184.194,36Other Gaseous Fossil NAGaseous Fossil Totals 184.194,36
� ��� 655.354,83Biomass total 99.959,26
Solid Biomass TJ 82.366,88 13.762,26 NA NA 96.129,14 1,00 NCV 96.129,14Liquid Biomass TJ 2.669,60 NA 2.669,60 NA NA 1,00 NCV NAGas Biomass TJ 3.830,11 NA NA NA 3.830,11 1,00 NCV 3.830,11
(1) To convert quantities in previous columns to energy units, use net calorific values (NCV) and write NCV in this column. If gross calorific values (GCV) are used, write GCV in this column.(2) If data for Anthracite are not available separately, include with Other Bituminous Coal.(3) BKB: Brown coal/peat briquettes.
% ������� �& �'
(�)#�
*�)����
Parties should provide detailed explanations on the fuel combustion sub-sector, including information relating to CO2 from the Reference approach, in the corresponding part of Chapter 3: Energy (CRF sub-sector 1.A) of the NIR. Uinformation and/or further details are needed to understand the content of this table.
(1) "Sectoral approach" is used to indicate the approach (if different from the Reference approach) used by the Party to estimate CO2 emissions from fuel combustion as reported in table 1.A(a), sheets 1-4.
(3) Apparent energy consumption data shown in this column are as in table 1.A(b).
(5) Emissions from biomass are not included.
����� �������� �!"
Parties should provide detailed explanations on the fuel combustion sub-sector, including information related to the comparison of CO2 emissions calculated using the Sectoral approach with those calculated using the Reference approach, in the
corresponding part of Chapter 3: Energy (CRF sub-sector 1.A) of the NIR. Use this documentation box to provide references to relevant sections of the NIR if any additional information and/or further details are needed to understand the content of this table.
If the CO2 emission estimates from the two approaches differ by more than 2 per cent, Parties should briefly explain the cause of this difference in this documentation box and provide a reference to relevant section of the NIR where this difference
is explained in more detail.
(2) Difference in CO2 emissions estimated by the Reference approach (RA) and the Sectoral approach (SA) (difference = 100% x ((RA-SA)/SA)). For calculating the difference in energy consumption between the two approaches, data as reported
in the column "Apparent energy consumption (excluding non-energy use and feedstocks)" are used for the Reference approach.
#�� " The Reporting Instructions of the Revised 1996 IPCC Guidelines for National Greenhouse Gas Inventories require that estimates of CO2 emissions from fuel combustion, derived using a detailed Sectoral approach, be compared to those
from the Reference approach (Worksheet 1-1 of the IPCC Guidelines, Volume 2, Workbook). This comparison is to assist in verifying the Sectoral data.
(4) For the purposes of comparing apparent energy consumption from the Reference approach with energy consumption from the Sectoral approach, Parties should, in this column, subtract from the apparent energy consumption (Reference approach) the energy content corresponding to the fuel quantities used as feedstocks and/or for non-energy purposes, in accordance with the accounting of energy use in the Sectoral approach
1.AC Difference - Reference and Sectoral Approach:Non-energy use of fuels is not included in the Danish National Approach. Fuel consumption for non-energy is subtracted in Reference Approach to maCO2 emission from plastic part of municipal wastes is included in the Danish National Approach. CO2 emission from the plastic part of municipal wastes is added in Reference Approach to make results comparable. (Other fuels of sources 1A1, 1A2 and 1A4)
����������������� �������������������
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458
��������� Fuel category correspondence list for the reference approach.
1) Changed test procedure at normal temperatures (40 s warm-up phase omitted) and for evaporation measurements 2) Less stringent emission limits for direct injection diesel engines 3) Unit: g/test
473
Light duty vehicles II (1305-1760 kg) ����� ������ ������ ��������� �������
Normal temp.
CO Gasoline 5,17 4,0 4,17 1,81
Diesel 5,17 1,25 0,80 0,63
HC Gasoline - - 0,25 0,13
NOx Gasoline - - 0,18 0,10
Diesel - - 0,65 0,33
HC+NOx Gasoline 1,4 0,6 - -
Diesel 1,4 1,0/1,32) 0,72 0,39
Particulates Diesel 0,19 0,12/0,142) 0,07 0,04
Low temp.
CO Gasoline - - - 24
HC Gasoline - - 2,7
Evaporation
HC3) Gasoline 2,0 2,0 2,0 2,0
1) Changed test procedure at normal temperatures (40 s warm-up phase omitted) and for evaporation measurements 2) Less stringent emission limits for direct injection diesel engines 3) Unit: g/test
474
Light duty vehicles III (>1760 kg) ����� ������ ������ ��������� �������
Normal temp.
CO Gasoline 6,9 5,0 5,22 2,27
Diesel 6,9 1,5 0,95 0,74
HC Gasoline - - 0,29 0,16
NOx Gasoline - - 0,21 0,11
Diesel - - 0,78 0,39
HC+NOx Gasoline 1,7 0,7 - -
Diesel 1,7 1,2/1,62) 0,86 0,46
Particulates Diesel 0,25 0,17/0,202) 0,10 0,06
Low temp.
CO Gasoline - - - 30
HC Gasoline - - - 3,2
Evaporation
HC3) Gasoline 2,0 2,0 2,0 2,0
1) Changed test procedure at normal temperatures (40 s warm-up phase omitted) and for evaporation measurements 2) Less stringent emission limits for direct injection diesel engines 3) Unit: g/test
1) Test procedure: Euro 1 og Euro 2: ECE (stationary)
Euro 3: ESC (stationary) + ELR (load response)
Euro 4, Euro 5 og EEV: ESC (stationary) + ETC (transient) + ELR (load response) 2) EEV: Emission limits for extra environmental friendly vehicles, used as a basis for economical incitaments (gas fueled vehicles). 3) For Euro 1, Euro 2 og Euro 3 less stringent emission limits apply for small engines:
������������Basis fuel use and emission factors, deterioration factors, transient factors and specific operational data for non road work-ing machinery and equipment, and recreational craft
Basis factors for diesel fuelled non road machinery
�������������Fuel use and emission factors, engine specific (NOx, CO, VOC (NMVOC and CH4)), and fuel type specific (S-%, SO2, PM) for ship engines
����������� ������ ������&'������ ����� �����(!"������������������ ������������������� High speed Medium speed Slow speed High speed Medium speed Slow speed
Grass- and clover fiel in rotation 32.3 - 10.0 2 26.2 6.26
Grass- and clover field out of rotation 38.8 - 20.0 - 20.0 3.53
Aftermath 6.3 - - - 1 6.3 0.76
Seeds of grass crops 6.3 10.7 - - 2 13.9 2.22
Set-a-side 38.8 - - 15.0 10 18.9 3.63
Total N from crop residue - 2003 53.10 a express the yield for 2005 - varies from year to year. Based on yield datta from Statistics Denmark and N-content from the feeding plan. Reference: Djurhuus and Hansen 2003
570
���������������
�������������������������
The starting year for the FOD model used is 1960 using historic data for waste amounts. The record of ISAG registration of waste amounts does not go back in time that far, but for the time-series 1990-2005 to be re-ported here this does not play a bigger role as regards time series consis-tency.
In Table 3E.1 results from the calculations by the model for selected years 1970-1979 to illustrate how the model performs: The left two co-lumns represent the time series of potential emissions. The actual emis-sions are in the next column as total. In the “from year”columns are put the contribution of emissions from individual previous years to the ac-tual years emission (the Total). So, the contribution from the deposited waste in 1970 with its potential emission in 1970 (=39.2) to the actual emissions in 1970 was 2.63. In 1971 the contribution from the 1970 depos-ited waste was 2.45. In 1972 it was 2.29 and so on. Summing up the con-tribution from the potential of the year 1970 until 1979 equals 19.6 corre-sponding to a half-life time of 10 years; i.e. half of the potential emission of 39.2 in 1970 is emitted after 10 years. The reason for in this illustration to go back in time to 1970 to 1779 is simply that this is a way in one illus-tration in a small table to illustrate these behaviours.
��������� Results from the FOD model 1970-1979
The result of summing this table horizontally in the “from years” col-umns is the total actual emission of that year.
����������������������������
����������Wastewater treated by wastewater treatment plants (WWTPs) comprises domestic and industrial wastewater as well as rainwater. 90% of the Danish household is connected to a municipal sewer system. The WWTPs have been upgraded significantly adoption of the Action Plan on the Aquatic Environment in 1987. The plan included more strict emis-
sion standards for nutrients and organic matter for WWTPs with a ca-pacity above 5000 person equivalents (PE) and, thus, rendered techno-logical upgrading of the majority of Danish WWTPs necessary.
In 2002 there was 1267 Danish WWTPs bigger than 30 PE, Table 3E.2. One PE expresses how much one person pollutes, i.e. 1 PE being defined as 21.9 kg BOD/year. BOD is the Biological Oxygen Demand, which is a measure of total degradable organic matter in the wastewater. The ca-pacities of WWTPs are calculated based on the amount of organic matter in the influent wastewater and converted to number of PEs irrespective of the origin of the wastewater, i.e. household or industry. Therefore it is not possible to calculate the emission contribution from industry and household separately. The per cent contribution from industry is, how-ever, known (cf. Table 3E.3).
��������� Size distributions of the Danish WWTPs in the year 2002.
WWTP capacity Number of WWTPs Load in % of total load on all WWTPs
>30 PE 1267 100
>500 PE 658 99
>2000 PE 441 98
>5000 PE 274 93
>15000 PE 130 83
>50000 PE 63 68
>100000 PE 30 48
In 1989 only 10% of the wastewater treatment processes included reduc-tion of N, P and BOD, in 1996 the number was 76%. Today 85% of the to-tal wastewater is treated at so-called MBNDC-WWTPs (i.e. WWTPS in-cluding Mechanical, Biological, Nitrification, Denitrification and Chemi-cal treatment processes), which is indicative of a high removal of N, P and DOC at the WWTPs (cf. Table 3E.4).
Since 1987 the fraction of industrial influent wastewater load at munici-pal and private WWTPs has increased from zero to around 40 % from 1999 and forward. The fraction of industrial sources discharges to city sewers contributing to the influent wastewater load in the national WWTPs are given in per cent based on PEs (1 PE = 60g BOD/day) in the Table 3E.3.
���������� The fraction of wastewater from industrial sources discharged to city sewers, i.e. industrial load of wastewater relative to total influent load at WWTPs*.
* based on information on influent loads in wastewater amounts and/or the amount of organic matter in the industry catchment area belonging to each WWTP.
Today, about one fifth of the biggest WWTPs treat around 90% of the to-tal volume of sewage in Denmark. Typically, these plants have mechani-cal treatment and biological treatment including removal of nitrogen and organic matter in activated sludge systems, a chemical precipitation step and finally settling of suspended particles in a clarifier tank. The chemi-cal processes include lime stabilisation. Many WWTPs are, in addition to
this, equipped with a filter or lagoon after the settling step. Overall stabi-lisation can be split into two processes, i.e. a biological and a chemical. The biological processes include anaerobic stabilisation where the sludge is digested in a digesting tank and aerobic stabilisation by long-term aeration (DEPA, 2002). In addition to hygienization, dewatering and sta-bilisation of the sludge, the sludge may be mineralised, composted, dried or combusted. Composting and sanitation is attributed by a storage time of 3 to 6 months. For plants with mineralization of sludge the storage time is about 10 years.
The wastewater treatment processes are divided into the following steps:
M = Mechanical B = Biological N = Nitrification (removal of nitrogen) D = Denitrification (removal of ammonia) C = Chemical
In general, the more steps the higher the cleaning level regarding nitro-gen, phosphorous and dissolved organic matter (DOC). The technologi-cal development and increased level of cleaning wastewater is clearly observed by the percentage reduction in the effluent amount of nitrogen, phosphor and DOC of 81%, 93% and 96% in 2003. The development in the effectiveness of reducing the nutrient content of the effluent waste-water is shown in Table 3E.4 at national level.
���������� Per cent reduction in nutrient content of effluent wastewater.
*DEPA has not yet released data from 2005.
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���������� � ������������������������ �������������The total organic degradable waste in kg BOD/year based on country-specific data is given in Table 3E.5. Activity data on influent TOW are needed in the unit of tonnes BOD /year, which is obtained by using total influent amount of water per year multiplied by the measured BOD in the inlet wastewater given in the second row of Table 3E.5-(DEPA 1994, 1996, 1997, 1998, 1999, 2001, 2002, 2003 and 2004). Numbers on BOD was provided by DEPA (personal communication for 1993, 2002-2004).
*BOD for the year 1993 is given in 1000 tonnes, whereas the amount of influent water is not given (DEPA, 1994).
** Calculated from country-specific COD data by use of BOD = COD/2.5.
*** TOWaverage=( TOWBOD+ TOWCOD)/2
TOW data from 2004 was obtained by personal communication with the DEPA.
The total organic waste in kg BOD/year based on the IPCC default method is given in Table 3E.6. The default region-specific TOW value is 18250 kg/ BOD/1000 persons/yr (IPCC GL, page 6.23, Table 6-5) for Eu-rope. The total organic degradable waste is estimated by multiplying the default value by the population number. In addition the default TOW data are increased by the percent contribution from the industry to in-vestigate the comparability by including this correction factor for the “missing” industrial contribution to the influent load TOW.
��������� Total degradable organic waste (TOW) calculated by use of the IPCC default BOD value for European countries.
By comparing the estimated TOW by use of country-specific data (cf. Ta-ble 3E.5) and TOW by use of default European data on the inlet BOD (cf. Table 3E.6), it can be observed that the default parameter method seems to underestimates the TOW. By increasing the default TOW data accord-ing to the industrial contribution to the total influent TOW the degree of underestimation becomes less significant as seen by comparing the TOW data for 1993, 1993 and 1994.
���������� Open triangles represents measured BOD data, open circles measured COD data, black triangles and circles, the measured BOD and COD data minus the reported indus-trial influents load, respectively. The grey triangles represent the TOW calculated based on the default IPCC method, and the brown triangles where the industrial influent load has been added. The BOD and COD derived TOW data representing household only shows that the BOD derived data has a steep slope which would make the influent load from household be-come negative around 1995. The BOD derived TOW data point from 1993 fits the brown data point nicely. The default methodology adding the percent corresponding to the industrial influ-ent was used form 1990 to 1998, after which the average of the, national statistics on meas-ured BOD and COD reported TOW data was used (blue crosses).
Based on mean values and standard deviation of TOW from Table 3E.5 and last row of Table 3E.6, an estimate of the maximum uncertainty on TOW is 20 %. It was determined to use the default IPPC methodology corrected for the industrial contribution to the influent TOW for the years 1990 to 1998 after which an average of national statistics on meas-ured BOD, COD multiplied by the influent amount of water as given in Table 3E.5 above. The gross emission is hereafter calculated by multiply-ing the TOW data with the emission factor of 0.15 kg CH4 / kg BOD (re-sults shown in the main report in Table 8.13 and illustrated in Figure 8.1).
������������������������������������ ���The calculated theoretical CH4 not emitted are given in Table 3E.7 below.
575
���������� Theoretical CH4 amount not emitted to the atmosphere [Gg] Country-specific data Regression by interpolation Reported data (CRF)
Country-specific activity data extracted from DEPA reports are given in the column 2 to 5. Due to missing data linear regression was performed based on the country-specific CH4 potentials not emitted from 1990 to 2002 as illustrated in Figure 3.E.2.
����������� From top to bottom based on 1987 data points: The upper regression line represents the total methane potential not emitted. The grey triangles and decreasing re-gression line represents the trend in internal combusting. The open triangles and regres-sion line of insignificant slope represents external combustion. The black quadrants and increasing regression line represents the methane potential internal combusted and re-used for production of sandblasting. Lastly the open quadrants and regression line with no or slightly positive slope represents the methane potential used for biogas production.
576
Filling of data gaps by use of linear regression function are not optimal due to changing trends in the final disposal categories influenced by na-tional political intervention strategies to improve water, soil and air quality. However, at this point this is the best available approach and es-timated methane potential according to regression by interpolation are given in column 6 to 9. For the years where national activity data are available, average of the actual and estimated numbers by interpolation have been reported; cf. columns 10 to 13 and Table 8.13 in the main re-port.
The yearly fluctuation in data used for simple linear regression of the av-erage trends in time is high as illustrated in Figure 3.E.2. Based on the percent distance between country-specific data to regression line, an es-timate of the average uncertainty is around 30%. The minimum uncer-tainty estimated for internal combustion is around 25%, while the uncer-tainty for external combustion. combustion for production of sandblast-ing product and biogas is around 70%. The variations/uncertainties are originating from the activity data given in Table 8.17 in the main report.
Based on the targets for final sludge disposal categories defined in the “Waste strategy 2004-2008 (The Danish Government, 2002), the internal and external combustion is expected to have reached a constant level, as the potential CH4 emission are already below a level corresponding to the target value for 2008 for this disposal category corresponding to a methane potential of 6.20 Gg (cf. Thomsen & Lyck, 2005).
The decrease in the amount of combusted sludge is partly accompanied by an increase in the reuse of sludge mainly in the production of sand-blasting products. The methane potential internal combusted and reused for production of sandblasting products are therefore expected to in-crease according to the national waste strategy (The Danish Government, 2002). The best available approach is at this stage to use the simple linear regression for this final disposal category as the defined target, corre-sponds to a methane potential of 7.75 Gg, have not yet been reached (cf. Thomsen& Lyck, 2005).
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������������������� �������� ������� ������ ����A German estimate of the emission factor for direct emission of N2O from wastewater treatment processes, not including industrial influents, is 7 g N2O / person per year (Schøn et al, 1993). In an investigation for the Netherlands, the emission factor is suggested to be 3.2 g N2O / per-son per year (Czepiel, Crill and Harries, 1995). Similar to the German es-timated EF, this emission factor does not account for co-discharges of in-dustrial nitrogen. To take into account the contribution from non-household nitrogen, Scheehle and Doorn (1997) suggest using the differ-ence between residential (decentralised) WWTPs and the centralised loading averages of influent nitrogen. As the decentralised WWTPs are assumed to have no influent wastewater load from the industry, whereas the centralised WWTPs receives most of the industrial wastewater, the difference in average influent loads may be used to derive an estimate of the fraction of industrial nitrogen influent load. The estimated fraction of industrial influent nitrogen load is used in combination with the Nether-lands emission factor to arrive at an EF corrected for industrial influent nitrogen load. In the United States a correction factor of 1.25 was ob-
577
tained resulting in an emission factor of (1.25*3.2) 4 g N2O / person per year (Scheehle and Doorn, 1997) including the contribution from indus-trial nitrogen influent load. An analogue approach has been used for cal-culating the Danish direct emission of N2O upon wastewater treatment.
Key data on nitrogen influent load distribution according to small, me-dium and large WWTPs are available from the Danish Water and Wastewater Association (Danva. 2001). The data are based on 20-25 WWTPs located in five big city areas in Denmark and are reported for the years 1998 to 2001. Based on these data an average factor of 3.52 was calculated as the average influent nitrogen for the large (centralised) WWTPs minus the average influent nitrogen load for the medium (de-centralised) WWTPs divided by the average nitrogen load for the me-dium WWTPs.
���������� Correction factors (CF) to adjust the emission factor (EF) to include influent loads of N to WWTPs from industry.
The use of this factor to correct the emission factor based on household wastewater only is based on the assumption that the emission factor is the same for household and industrial wastewater respectively. The cor-rection factor in 1987 is equal to 1 corresponding to zero contribution from industry. Emission factors are equal to CF * 3.2 g N2O / person per year. The average resulting emission factor for direct emission of N2O is (3.52*3.2) 11.3 g N2O / person per year. However, the contribution to the Danish WWTPs from industry has changed from close to zero in 1984 up to an average of 42 % since 1998. Therefore, the per cent industrial wastewater influent loads from 1987 (where it was zero) and the years 1998 to 2001, for which a corrected emission factor can be estimated, was used in a simple regression of % industrial wastewater influent load ver-sus the corrected emission factors. Regression equation 1 was used for estimating the emission factor for all years 1990-2002.
Eq. 1: ��������������� = 0.1887 * � + 3.2816
where � is the per cent industrial influent load.
From 2004 and forward an average of the calculated EFs from 2000 to 2003 was applied as industrial contribution to the influent TOW is as-sumed constant and it seemed most appropriate therefore to use an av-erage of 10.8 reducing the effect from the highest industrial contributions to the influent TOW in 1998 and 1999 (cf. Table 8.18 in the main report).
year WWTP-large
[ton N / year]
WWTP-medium
[ton N/year]
CF EF
[N2O /capita per year]
1987 1 3.2
1998 1081 233 3.64 11.7
1999 1042 220 3.74 12.0
2000 1016 222 3.58 11.5
2001 894 216 3.14 10.0
578
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National Atmospheric Inventory http://www.aeat.co.uk/netcen/airqual/naei/annreport/annrep99/app1_28.html
The emission inventory for Great Britain is performed by the National Environmental Technology Centre, June 2000, and covers the following sectors
Total emission Energy Production Comm+ Residn Combusn. Industrial Combustion Production Processes Extr & Distrib of Fossil Fuels Solvent Use Road Transport Other Transp & Mach Waste Treatment & Disp Nature (Forests)
The Danish greenhouse gas emission inventories for 1990-2005 include all sources identified by the Revised IPPC Guidelines except the follow-ing:
Agriculture: The methane conversion factor in relation to the enteric fermentation for poultry and fur farming is not estimated. There is no default value recommended in IPCC GPG (Table A-4). However, this emission is seen as non-significant compared with the total emission from enteric fermentation.
In NIR reports up until the NIR 2004 we included the full CRF tables in the NIR report itself as well as we submitted the CRF as spreadsheet fi-les. In this NIR we only include the trend tables 1990-2005 (CRF Table 10 sheet 1-5) as they appear in the CRF 2005 spreadsheet file, Tables 10.1-10.5. The full CRF tables 1990-2005 as spreadsheets are submitted sepa-rately as well as the files in the new CRF reporter tools. Notice that this tool defines base year in the sense of the Climate Change Convention (not as in the Kyoto protocol) which is the emissions in 1990.
The Tables enclosed in this Annex are for the Kingdom of Denmark, i.e. Denmark, Faroe Islands and Greenland (Annex 6.1.1) and for Denmark and Greenland (Annex 6.1.2). Emissions for Faroe Islands and Greenland are entered under the Category “7. Other”.
A. Mineral Products 1.072,21 1.246,16 1.365,58 1.383,34 1.406,38 1.406,93 1.517,08 1.685,28 1.682,42 1.609,93B. Chemical Industry 0,80 0,80 0,80 0,80 0,80 0,80 1,45 0,87 0,56 0,58C. Metal Production 28,45 28,45 28,45 30,97 33,50 38,56 35,19 35,01 42,19 43,04D. Other Production NE NE NE NE NE NE NE NE NE NEE. Production of Halocarbons and SF6
A. Enteric FermentationB. Manure ManagementC. Rice CultivationD. Agricultural Soils E. Prescribed Burning of SavannasF. Field Burning of Agricultural Residues
A. Mineral Products 1.640,36 1.660,41 1.696,16 1.571,22 1.728,29 1.640,82 53,03B. Chemical Industry 0,65 0,83 0,55 1,05 3,01 3,01 275,75C. Metal Production 40,73 46,68 NA,NO NA,NO NA,NO 15,58 -45,22D. Other Production NE NE NE NE NE NE 0,00E. Production of Halocarbons and SF6
A. Enteric FermentationB. Manure ManagementC. Rice CultivationD. Agricultural Soils E. Prescribed Burning of SavannasF. Field Burning of Agricultural Residues
A. Mineral Products IE,NA IE,NA IE,NA IE,NA IE,NA IE,NA IE,NA IE,NA IE,NA IE,NAB. Chemical Industry NA,NO NA,NO NA,NO NA,NO NA,NO NA,NO NA,NO NA,NO NA,NO NA,NOC. Metal Production NA,NO NA,NO NA,NO NA,NO NA,NO NA,NO NA,NO NA,NO NA,NO NA,NOD. Other ProductionE. Production of Halocarbons and SF6
A. Forest Land NE,NO NE,NO NE,NO NE,NO NE,NO NE,NO NE,NO NE,NO NE,NO NE,NOB. Cropland NA,NO NA,NO NA,NO NA,NO NA,NO NA,NO NA,NO NA,NO NA,NO NA,NOC. Grassland NO NO NO NO NO NO NO NO NO NOD. Wetlands -0,03 -0,03 -0,03 -0,03 -0,03 -0,03 -0,03 -0,03 -0,03 -0,02E. Settlements NE,NO NE,NO NE,NO NE,NO NE,NO NE,NO NE,NO NE,NO NE,NO NE,NOF. Other Land NE,NO NE,NO NE,NO NE,NO NE,NO NE,NO NE,NO NE,NO NE,NO NE,NOG. Other NO NO NO NO NO NO NO NO NO NO
A. Mineral Products IE,NA IE,NA IE,NA IE,NA IE,NA IE,NA 0,00B. Chemical Industry NA,NO NA,NO NA,NO NA,NO NA,NO NA,NO 0,00C. Metal Production NA,NO NA,NO NA,NO NA,NO NA,NO NA,NO 0,00D. Other ProductionE. Production of Halocarbons and SF6
A. Forest Land NE,NO NE,NO NE,NO NE,NO NE,NO NE,NO 0,00B. Cropland NA,NO NA,NO NA,NO NA,NO NA,NO NA,NO 0,00C. Grassland NO NO NO NO NO NO 0,00D. Wetlands -0,02 -0,02 -0,02 -0,02 -0,02 -0,02 -17,33E. Settlements NE,NO NE,NO NE,NO NE,NO NE,NO NE,NO 0,00F. Other Land NE,NO NE,NO NE,NO NE,NO NE,NO NE,NO 0,00G. Other NO NO NO NO NO NO 0,00
A. Mineral Products IE,NA IE,NA IE,NA IE,NA IE,NA IE,NA IE,NA IE,NA IE,NA IE,NAB. Chemical Industry 3,36 3,08 2,72 2,56 2,60 2,92 2,69 2,74 2,60 3,07C. Metal Production NA NA NA NA NA NA NA NA NA NAD. Other ProductionE. Production of Halocarbons and SF6
F. Consumption of Halocarbons and SF6
G. Other NA NA NA NA NA NA NA NA NA NA
���" �#��������$�%����� ��!��&��� NA NA NA NA NA NA NA NA NA NA����'���!������ ���� ����� ����� ���� ��� � ����� ����� ��� ���� �����
A. Enteric FermentationB. Manure Management 2,21 2,20 2,21 2,24 2,17 2,10 2,10 2,10 2,12 2,06C. Rice CultivationD. Agricultural Soils 26,94 26,45 25,46 24,84 24,18 23,58 22,48 22,20 22,07 20,71E. Prescribed Burning of Savannas NA NA NA NA NA NA NA NA NA NAF. Field Burning of Agricultural Residues NA,NO NA,NO NA,NO NA,NO NA,NO NA,NO NA,NO NA,NO NA,NO NA,NOG. Other NA NA NA NA NA NA NA NA NA NA
A. Mineral Products IE,NA IE,NA IE,NA IE,NA IE,NA IE,NA 0,00B. Chemical Industry 3,24 2,86 2,50 2,89 1,71 NA,NO -100,00C. Metal Production NA NA NA NA NA NA 0,00D. Other ProductionE. Production of Halocarbons and SF6
F. Consumption of Halocarbons and SF6
G. Other NA NA NA NA NA NA 0,00
����,��-������*�����#���!$��.��� NA NA NA NA NA NA 0,00����)�"$!��!�� ���'� ����& ���&� ����& ����� ����� +�����
A. Enteric FermentationB. Manure Management 1,96 1,97 1,92 1,83 1,85 1,80 -18,56C. Rice CultivationD. Agricultural Soils 19,97 19,49 18,71 18,43 18,46 18,31 -32,03E. Prescribed Burning of Savannas NA NA NA NA NA NA 0,00F. Field Burning of Agricultural Residues NA,NO NA,NO NA,NO NA,NO NA,NO NA,NO 0,00G. Other NA NA NA NA NA NA 0,00
(3) Enter actual emissions estimates. If only potential emissions estimates are available, these should be reported in this table and an indication for this be provided in the documentation box. Only in these rows are the emissions expressed as CO2 equivalent emissions.
(4) In accordance with the UNFCCC reporting guidelines, HFC and PFC emissions should be reported for each relevant chemical. However, if it is not possible to report values for each chemical (i.e. mixtures, confidential data, lack of disaggregation), this row could be used for reporting aggregate figures for HFCs and PFCs, respectively. Note that the unit used for this row is Gg of CO2 equivalent
and that appropriate notation keys should be entered in the cells for the individual chemicals.
�())*+�#,)��-,�)./,,/�*,
�())*+�#,)��-,�,�#(�)�-*0�,/*1��-�)��(/),
(1) The column "Base year" should be filled in only by those Parties with economies in transition that use a base year different from 1990 in accordance with the relevant decisions of the COP. For these Parties, this different base year is used to calculate the percentage change in the final column of this table.
(2) Fill in net emissions/removals as reported in table Summary 1.A. For the purposes of reporting, the signs for removals are always negative (-) and for emissions positive (+).
(1) The column "Base year" should be filled in only by those Parties with economies in transition that use a base year different from 1990 in accordance with the relevant decisions of the COP. For these Parties, this different base year is used to calculate the percentage change in the final column of this table.
(2) Fill in net emissions/removals as reported in table Summary 1.A. For the purposes of reporting, the signs for removals are always negative (-) and for emissions positive (+).
(3) Enter actual emissions estimates. If only potential emissions estimates are available, these should be reported in this table and an indication for this be provided in the documentation box. Only in these rows are the emissions expressed as CO2 equivalent emissions.
(4) In accordance with the UNFCCC reporting guidelines, HFC and PFC emissions should be reported for each relevant chemical. However, if it is not possible to report values for each chemical (i.e. mixtures, confidential data, lack of disaggregation), this row could be used for reporting aggregate figures for HFCs and PFCs, respectively. Note that the unit used for this row is Gg of CO2 equivalent
and that appropriate notation keys should be entered in the cells for the individual chemicals.
A. Mineral Products 1.072,21 1.246,16 1.365,58 1.383,34 1.406,38 1.406,93 1.517,08 1.685,28 1.682,42 1.609,93B. Chemical Industry 0,80 0,80 0,80 0,80 0,80 0,80 1,45 0,87 0,56 0,58C. Metal Production 28,45 28,45 28,45 30,97 33,50 38,56 35,19 35,01 42,19 43,04D. Other Production NE NE NE NE NE NE NE NE NE NEE. Production of Halocarbons and SF6
A. Enteric FermentationB. Manure ManagementC. Rice CultivationD. Agricultural Soils E. Prescribed Burning of SavannasF. Field Burning of Agricultural Residues
A. Mineral Products 1.640,36 1.660,41 1.696,16 1.571,22 1.728,29 1.640,82 53,03B. Chemical Industry 0,65 0,83 0,55 1,05 3,01 3,01 275,75C. Metal Production 40,73 46,68 NA,NO NA,NO NA,NO 15,58 -45,22D. Other Production NE NE NE NE NE NE 0,00E. Production of Halocarbons and SF6
A. Enteric FermentationB. Manure ManagementC. Rice CultivationD. Agricultural Soils E. Prescribed Burning of SavannasF. Field Burning of Agricultural Residues
A. Mineral Products IE,NA IE,NA IE,NA IE,NA IE,NA IE,NA IE,NA IE,NA IE,NA IE,NAB. Chemical Industry NA,NO NA,NO NA,NO NA,NO NA,NO NA,NO NA,NO NA,NO NA,NO NA,NOC. Metal Production NA,NO NA,NO NA,NO NA,NO NA,NO NA,NO NA,NO NA,NO NA,NO NA,NOD. Other ProductionE. Production of Halocarbons and SF6
A. Forest Land NE,NO NE,NO NE,NO NE,NO NE,NO NE,NO NE,NO NE,NO NE,NO NE,NOB. Cropland NA,NO NA,NO NA,NO NA,NO NA,NO NA,NO NA,NO NA,NO NA,NO NA,NOC. Grassland NO NO NO NO NO NO NO NO NO NOD. Wetlands -0,03 -0,03 -0,03 -0,03 -0,03 -0,03 -0,03 -0,03 -0,03 -0,02E. Settlements NE,NO NE,NO NE,NO NE,NO NE,NO NE,NO NE,NO NE,NO NE,NO NE,NOF. Other Land NE,NO NE,NO NE,NO NE,NO NE,NO NE,NO NE,NO NE,NO NE,NO NE,NOG. Other NO NO NO NO NO NO NO NO NO NO
A. Mineral Products IE,NA IE,NA IE,NA IE,NA IE,NA IE,NA 0,00B. Chemical Industry NA,NO NA,NO NA,NO NA,NO NA,NO NA,NO 0,00C. Metal Production NA,NO NA,NO NA,NO NA,NO NA,NO NA,NO 0,00D. Other ProductionE. Production of Halocarbons and SF6
A. Forest Land NE,NO NE,NO NE,NO NE,NO NE,NO NE,NO 0,00B. Cropland NA,NO NA,NO NA,NO NA,NO NA,NO NA,NO 0,00C. Grassland NO NO NO NO NO NO 0,00D. Wetlands -0,02 -0,02 -0,02 -0,02 -0,02 -0,02 -17,33E. Settlements NE,NO NE,NO NE,NO NE,NO NE,NO NE,NO 0,00F. Other Land NE,NO NE,NO NE,NO NE,NO NE,NO NE,NO 0,00G. Other NO NO NO NO NO NO 0,00
A. Mineral Products IE,NA IE,NA IE,NA IE,NA IE,NA IE,NA IE,NA IE,NA IE,NA IE,NAB. Chemical Industry 3,36 3,08 2,72 2,56 2,60 2,92 2,69 2,74 2,60 3,07C. Metal Production NA NA NA NA NA NA NA NA NA NAD. Other ProductionE. Production of Halocarbons and SF6
F. Consumption of Halocarbons and SF6
G. Other NA NA NA NA NA NA NA NA NA NA
���" �#��������$�%����� ��!��&��� NA NA NA NA NA NA NA NA NA NA����'���!������ ���� ����� ����� ���� ��� � ����� ����� ��� ���� �����
A. Enteric FermentationB. Manure Management 2,21 2,20 2,21 2,24 2,17 2,10 2,10 2,10 2,12 2,06C. Rice CultivationD. Agricultural Soils 26,94 26,45 25,46 24,84 24,18 23,58 22,48 22,20 22,07 20,71E. Prescribed Burning of Savannas NA NA NA NA NA NA NA NA NA NAF. Field Burning of Agricultural Residues NA,NO NA,NO NA,NO NA,NO NA,NO NA,NO NA,NO NA,NO NA,NO NA,NOG. Other NA NA NA NA NA NA NA NA NA NA
A. Mineral Products IE,NA IE,NA IE,NA IE,NA IE,NA IE,NA 0,00B. Chemical Industry 3,24 2,86 2,50 2,89 1,71 NA,NO -100,00C. Metal Production NA NA NA NA NA NA 0,00D. Other ProductionE. Production of Halocarbons and SF6
F. Consumption of Halocarbons and SF6
G. Other NA NA NA NA NA NA 0,00
����,��-������*�����#���!$��.��� NA NA NA NA NA NA 0,00����)�"$!��!�� ���'� ����& ���&� ����& ����� ����� +�����
A. Enteric FermentationB. Manure Management 1,96 1,97 1,92 1,83 1,85 1,80 -18,56C. Rice CultivationD. Agricultural Soils 19,97 19,49 18,71 18,43 18,46 18,31 -32,03E. Prescribed Burning of Savannas NA NA NA NA NA NA 0,00F. Field Burning of Agricultural Residues NA,NO NA,NO NA,NO NA,NO NA,NO NA,NO 0,00G. Other NA NA NA NA NA NA 0,00
(3) Enter actual emissions estimates. If only potential emissions estimates are available, these should be reported in this table and an indication for this be provided in the documentation box. Only in these rows are the emissions expressed as CO2 equivalent emissions.
(4) In accordance with the UNFCCC reporting guidelines, HFC and PFC emissions should be reported for each relevant chemical. However, if it is not possible to report values for each chemical (i.e. mixtures, confidential data, lack of disaggregation), this row could be used for reporting aggregate figures for HFCs and PFCs, respectively. Note that the unit used for this row is Gg of CO2 equivalent
and that appropriate notation keys should be entered in the cells for the individual chemicals.
�())*+�#,)��-,�)./,,/�*,
�())*+�#,)��-,�,�#(�)�-*0�,/*1��-�)��(/),
(1) The column "Base year" should be filled in only by those Parties with economies in transition that use a base year different from 1990 in accordance with the relevant decisions of the COP. For these Parties, this different base year is used to calculate the percentage change in the final column of this table.
(2) Fill in net emissions/removals as reported in table Summary 1.A. For the purposes of reporting, the signs for removals are always negative (-) and for emissions positive (+).
(3) Enter actual emissions estimates. If only potential emissions estimates are available, these should be reported in this table and an indication for this be provided in the documentation box. Only in these rows are the emissions expressed as CO2 equivalent emissions.
(4) In accordance with the UNFCCC reporting guidelines, HFC and PFC emissions should be reported for each relevant chemical. However, if it is not possible to report values for each chemical (i.e. mixtures, confidential data, lack of disaggregation), this row could be used for reporting aggregate figures for HFCs and PFCs, respectively. Note that the unit used for this row is Gg of CO2 equivalent
and that appropriate notation keys should be entered in the cells for the individual chemicals.
�.//01�$2/��32�/45225�02
�.//01�$2/��32�2�$.�/�306�2507��3!/��.5/2
(1) The column "Base year" should be filled in only by those Parties with economies in transition that use a base year different from 1990 in accordance with the relevant decisions of the COP. For these Parties, this different base year is used to calculate the percentage change in the final column of this table.
(2) Fill in net emissions/removals as reported in table Summary 1.A. For the purposes of reporting, the signs for removals are always negative (-) and for emissions positive (+).
In the Faroe Islands a major work was made in 2002 to produce a revised and more comprehensive greenhouse gas inventory as required by the IPCC guidelines (Lastein et al., 2003). The work comprised emission es-timates of CO2, CH4 and N2O for the years 1990-2001.
An update of this work has recently been made (Heilsufrøðiliga Starvss-tovan, 2005). The results reported in the latter work are however incom-plete as regards the emission sources included, and thus the current 2002 estimate is used also for 2003, 2004 and 2005.
The significant increase in CO2 emissions from 1998 to 2001 is mainly due to more fuel use in the fishery, public electricity and manufacturing industry sectors, while the CH4 and N2O emission increases (the Faroe Islands) are due to rising activity in the agricultural sector.
For Greenland the inventory has been expanded to include emissions from agriculture and consumption of F-gases and the pollutants CH4, N2O, HFCs, CO, NMVOC and NOx. However, fossil fuels are still the most important sources of greenhouse gases in this region. Figures for CO2, CH4, and N2O emissions from 1990 to 2005 and for HFCs for 1995-2005 are given in the table below. The inventory is based on information from e.g. KNI Pilersuisoq, Statoil, Nukissiorfiit Årsoversigt 2004, Grøn-lands Kommando (Greenland Command) and Konsulenttjenesten for Landbrug (Consultancy for Agriculture). The methodology applied is described in Annex 6.2.1.
615
The CO2 emission for 1993 is intrapolated.
�����������
Heilsufrøðiliga Starvsstovan 2005: Útlát av veðurlagsgassi í Føroyum – Uppgerð dagført fram til 2003. Heilsufrøðiliga Starvsstovan: 20 pp. Avai-lable at: http://www.hfs.fo/tíðindaskriv/tidindi.asp.
Lastein, L. & Winther, M. 2003: Emission of greenhouse gases and long-range transboundary air pollutants in the Faroe Islands 1990-2001. Na-tional Environmental Research Institute. - NERI Technical Report 477 (electronic): 62 pp. Available at: http://www.dmu.dk/1_viden/2_Pub-likationer/3_fagrapporter/rapporter/FR477.PDF
��������� Estimation of greenhouse gas emissions in Greenland and the Faroe Is-lands 1990-2004.
The GHG inventory for Greenland includes the following sectors:
• Energy sector • Industrial processes (consumption of F-gasses) • Agriculture (sheep) • Solid waste management (incineration without energy recovery, dis-
posal, and open burning)
The applied methodology do to a large degree follow the methodology applied in the Danish inventory, however, the availability of data – espe-cially site specific data – do allow the same equations to used for all the sectors. The actual methodology is described below for the different sec-tors. The data handling and calculations were performed by use of the IPCC excel tool (version 1.1). The excel tool were modified/extended to cover all relevant processes and calculations were corrected where it was found necessary.
+���()��������The inventory covering the energy sector has been performed according to the IPCC tier 1 methodology. The CO2 emission has been calculated by using the methodology included in the IPCC software. This method-ology implies use of C content per fuel type (default) and fraction of car-bon oxidised (default); see the equation below.
12/44,2×××Σ= ��������
�����
where:
Actfuel = activity; consumption of fuel a
EFC, fuel = C emission factor for fuel a
Ox = oxidation factor
The emissions of CH4, N2O, NOx, CO, and NMVOC have been calculated at sector/fuel level by using IPCC default emission factors combined with measured/Danish EF for waste incineration (with energy recovery). The equation applied for each pollutant is:
)(����
������ ×Σ=
where:
EF = emission factor
Act = activity; fuel input
617
a = fuel type
b = sector activity
������ ������������The inventory covering industrial gasses has been performed according to tier 2 with estimates of the actual emissions. Information on emission of industrial gasses is only available from 1995 onwards.
�(���������Agriculture is sparse in Greenland due to climatic conditions, however sheep are considered to contribute to emission of greenhouse gasses. En-teric fermentation and manure management is assumed to contribute to emission of CH4 and nitrogen excretion is assumed to contribute to emis-sion of N2O.
The equations used are presented below.
)( ....4 �������������� ������ +×=
28/4422 . ×××= ����������� �����
The applied emission factors are presented in Table 0.2.
��������� Applied emission factors1 for agriculture.
Enteric fermentation
kg CH4/head/year
Manure management
kg CH4/head/year
Nitrogen excretion
kg N/head/year
Sheep 17.17 0.32 16.87
The emission factors are adopted from the Danish inventory (Illerup et al., 2006).
The IPCC default for EFN2O: 0.02 kg N2O-N/kg N has been chosen.
,���� ����� � (������The solid waste management in Greenland can be divided in the follow-ing processes:
• Waste incineration with energy recovery • Waste incineration without energy recovery • Managed waste disposal combined with open burning • Un-managed waste disposal combined with open burning Information on amount of waste produced per year, amount of waste treated in the different processes, distribution between household and commercial waste, composition of the household waste and commercial waste, respectively, were provided by Ministry of Environment and Na-ture, Nuuk, Greenland; see Table 0.3. The distribution of waste between different treatment options after correction for open burning is presented in Table 0.4. The amount of household waste generated in 2005 is as-sumed to be the same as in 2004.
618
The calculation of anaerobe degradation at the waste disposal sites is done by use of the tier 1 methodology i.e. by using the following equa-tion:
)1(12/164
�������� ����
−×××××=
where:
MSW = amount of waste disposed of at managed/un-managed dis-posal sites
MCF = methane correction factor
DOC = degradable organic carbon
�������� Composition of municipal waste before and after open burning.
The emission factors applied in the calculation of emissions from incin-eration of waste with and without energy recovery and open burning are based on measured emissions combined with IPCC default emission fac-tors and Danish emission factors; see Table 0.5.
,��� �)�Time-series for the greenhouse gasses – see Table 0.6 – and for the differ-ent sectors – see Table 0.7 – as well as summary tables for the years 1990, 1995, and 2005 are presented below. CO2 is accounting for more than 95% of the emission of greenhouse gasses and the emission of green-house gasses is mainly related to the energy sector.
��������� Emission factors for incineration of waste.
P = Potential emissions based on Tier 1 Approach. A = Actual emissions based on Tier 2 Approach.
(1) For verification purposes, countries are asked to report the results of their calculations using the Reference Approach and explain any differences with the Sectoral Approach. Do not include the resultsof both the Reference Approach and the Sectoral Approach in national totals.
(2) The formula does not provide a total estimate of both CO2 emissions and CO2 removals. It estimates “net” emissions of CO2 and places a single number in either the CO2 emissions
or CO2 removals column, as appropriate. Please note that for the purposes of reporting, the signs for uptake are always (-) and for emissions (+). ��������� Short summary report for national greenhouse gas inventories 1990.
P = Potential emissions based on Tier 1 Approach. A = Actual emissions based on Tier 2 Approach.
(1) For verification purposes, countries are asked to report the results of their calculations using the Reference Approach and explain any differences with the Sectoral Approach. Do not include the resultsof both the Reference Approach and the Sectoral Approach in national totals.
(2) The formula does not provide a total estimate of both CO2 emissions and CO2 removals. It estimates “net” emissions of CO2 and places a single number in either the CO2 emissions
or CO2 removals column, as appropriate. Please note that for the purposes of reporting, the signs for uptake are always (-) and for emissions (+). ��������� Short summary report for national greenhouse gas inventories 1995
623
�����������Illerup, J.B., Lyck, E., Nielsen, O.-K., Mikkelsen, M.H., Hoffmann, L., Gyldenkærne, S., Nielsen, M., Sørensen, P.B., Fauser, P., Thomsen, M. & Winther, M. 2006: Denmark’s National Inventory Report 2006. Submitted under the United Nations Framework Convention on Climate Change, 1990-2004. NERI Tech-nical Report No. 589.
P = Potential emissions based on Tier 1 Approach. A = Actual emissions based on Tier 2 Approach.
(1) For verification purposes, countries are asked to report the results of their calculations using the Reference Approach and explain any differences with the Sectoral Approach. Do not include the resultsof both the Reference Approach and the Sectoral Approach in national totals.
(2) The formula does not provide a total estimate of both CO2 emissions and CO2 removals. It estimates “net” emissions of CO2 and places a single number in either the CO2 emissions
or CO2 removals column, as appropriate. Please note that for the purposes of reporting, the signs for uptake are always (-) and for emissions (+). ���������� Short summary report for national greenhouse gas inventories 2005.
Up until NIR 2004, NERI included the full CRF tables in the NIR report itself as well as the CRF submitted as spreadsheet files. In NIR 2005 and 2006 and the present year’s NIR (2007), only the trend tables 1990-2005 (CRF Table 10 sheet 1-5) have been included in the NIR as Tables A9.1-.5. These tables are copied from the CRF 2005 spreadsheet file, Tables 10.1-10.5. The full CRF tables 1990-2005 are submitted as spreadsheets sepa-rately, as well as the files in the new CRF reporter tool. Notice that this tool defines the base year regarding emissions in the sense of the Climate Change Convention (not as in the Kyoto protocol) which is the emissions in 1990.
A. Mineral Products 1.072,21 1.246,16 1.365,58 1.383,34 1.406,38 1.406,93 1.517,08 1.685,28 1.682,42 1.609,93B. Chemical Industry 0,80 0,80 0,80 0,80 0,80 0,80 1,45 0,87 0,56 0,58C. Metal Production 28,45 28,45 28,45 30,97 33,50 38,56 35,19 35,01 42,19 43,04D. Other Production NE NE NE NE NE NE NE NE NE NEE. Production of Halocarbons and SF6
A. Enteric FermentationB. Manure ManagementC. Rice CultivationD. Agricultural Soils E. Prescribed Burning of SavannasF. Field Burning of Agricultural Residues
2������������$5����� �� NO NO NO NO NO NO NO NO NO NO*$���/���� ���0� /��� /��� 4.640,89 5.032,95 5.321,34 5.574,45 5.533,46 5.868,80 6.295,78 6.542,43 6.491,97 6.857,21
�6��-7$&"���'"�"$&6*��'-8�"�-9�
*'.��$6��"
630
Table A9.1 continued ���������������� ��� Inventory 2005
A. Mineral Products 1.640,36 1.660,41 1.696,16 1.571,22 1.728,29 1.640,82 53,03B. Chemical Industry 0,65 0,83 0,55 1,05 3,01 3,01 275,75C. Metal Production 40,73 46,68 NA,NO NA,NO NA,NO 15,58 -45,22D. Other Production NE NE NE NE NE NE 0,00E. Production of Halocarbons and SF6
A. Enteric FermentationB. Manure ManagementC. Rice CultivationD. Agricultural Soils E. Prescribed Burning of SavannasF. Field Burning of Agricultural Residues
A. Mineral Products IE,NA IE,NA IE,NA IE,NA IE,NA IE,NA IE,NA IE,NA IE,NA IE,NAB. Chemical Industry NA,NO NA,NO NA,NO NA,NO NA,NO NA,NO NA,NO NA,NO NA,NO NA,NOC. Metal Production NA,NO NA,NO NA,NO NA,NO NA,NO NA,NO NA,NO NA,NO NA,NO NA,NOD. Other ProductionE. Production of Halocarbons and SF6
A. Mineral Products IE,NA IE,NA IE,NA IE,NA IE,NA IE,NA 0,00B. Chemical Industry NA,NO NA,NO NA,NO NA,NO NA,NO NA,NO 0,00C. Metal Production NA,NO NA,NO NA,NO NA,NO NA,NO NA,NO 0,00D. Other ProductionE. Production of Halocarbons and SF6
A. Mineral Products IE,NA IE,NA IE,NA IE,NA IE,NA IE,NA IE,NA IE,NA IE,NA IE,NAB. Chemical Industry 3,36 3,08 2,72 2,56 2,60 2,92 2,69 2,74 2,60 3,07C. Metal Production NA NA NA NA NA NA NA NA NA NAD. Other ProductionE. Production of Halocarbons and SF6
F. Consumption of Halocarbons and SF6
G. Other NA NA NA NA NA NA NA NA NA NA ���" �#��������$�%����� ��!��&��� NA NA NA NA NA NA NA NA NA NA����'���!������ ���� ����� ����� ���� ��� � ����� ����� ��� ���� �����
A. Enteric FermentationB. Manure Management 2,21 2,20 2,21 2,24 2,17 2,10 2,10 2,10 2,12 2,06C. Rice CultivationD. Agricultural Soils 26,94 26,45 25,46 24,84 24,18 23,58 22,48 22,20 22,07 20,71E. Prescribed Burning of Savannas NA NA NA NA NA NA NA NA NA NAF. Field Burning of Agricultural Residues NA,NO NA,NO NA,NO NA,NO NA,NO NA,NO NA,NO NA,NO NA,NO NA,NOG. Other NA NA NA NA NA NA NA NA NA NA
A. Mineral Products IE,NA IE,NA IE,NA IE,NA IE,NA IE,NA 0,00B. Chemical Industry 3,24 2,86 2,50 2,89 1,71 NA,NO -100,00C. Metal Production NA NA NA NA NA NA 0,00D. Other ProductionE. Production of Halocarbons and SF6
F. Consumption of Halocarbons and SF6
G. Other NA NA NA NA NA NA 0,00
���-��.��������+�����%���#&��/��� NA NA NA NA NA NA 0,00����*��$&#��#�� �� � �� �( � (� � �( � � � �� , � �
A. Enteric FermentationB. Manure Management 1,96 1,97 1,92 1,83 1,85 1,80 -18,56C. Rice CultivationD. Agricultural Soils 19,97 19,49 18,71 18,43 18,46 18,31 -32,03E. Prescribed Burning of Savannas NA NA NA NA NA NA 0,00F. Field Burning of Agricultural Residues NA,NO NA,NO NA,NO NA,NO NA,NO NA,NO 0,00G. Other NA NA NA NA NA NA 0,00
����0����/�� �0���,/��������������1������� ,�!
A. Forest Land IE,NE,NO IE,NE,NO IE,NE,NO IE,NE,NO IE,NE,NO IE,NE,NO 0,00B. Cropland NA,NO NA,NO NA,NO NA,NO NA,NO NA,NO 0,00C. Grassland NO NO NO NO NO NO 0,00D. Wetlands 0,00 0,00 0,00 0,00 0,00 0,00 -17,33E. Settlements NE,NO NE,NO NE,NO NE,NO NE,NO NE,NO 0,00F. Other Land NE,NO NE,NO NE,NO NE,NO NE,NO NE,NO 0,00
G. Other NO NO NO NO NO NO 0,00(���2���� �� �' � �( �! � ,
National Environmental Research Institute, NERI, is a part of
University of Aarhus.
NERI’s tasks are primarily to conduct research, collect data, and give advice
on problems related to the environment and nature.
At NERI’s website www.neri.dk you’ll fi nd information regarding ongoing research and development projects.
Furthermore the website contains a database of publications including scientifi c articles, reports, conference contributions etc. produced by NERI staff members.
National Environmental Research InstituteDanmarks Miljøundersøgelser
NERIDMU
Further information: www.neri.dk
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639
NERI Technical Reports
NERI’s website www.neri.dk contains a list of all published technical reports along with other NERI publications. All recent reports can be downloaded in electronic format (pdf) without charge. Some of the Danish reports include an English summary.
Nr./No. 2007
630 Control of Pesticides 2005. Chemical Substances and Chemical Preparations. By Krongaard, T., Petersen, K.K. & Christoffersen, C. 24 pp.
629 A chemical and biological study of the impact of a suspected oil seep at the coast of Marraat, Nuussuaq, Greenland. With a summary of other environmental studies of hydrocarbons in Greenland. By Mosbech, A. et al. 55 pp.
628 Danish Emission Inventories for Stationary Combustion Plants. Inventories until year 2004. By Nielsen, O.-K., Nielsen, M. & Illerup, J.B. 176 pp.
627 Verifi cation of the Danish emission inventory data by national and international data comparisons. By Fauser, P. et al. 51 pp.
626 Trafi kdræbte større dyr i Danmark – kortlægning og analyse af påkørselsforhold. Af Andersen, P.N. & Madsen, A.B. 58 s.
625 Virkemidler til realisering af målene i EU’s Vandrammedirektiv. Udredning for udvalg nedsat af Finansministeriet og Miljøministeriet: Langsigtet indsats for bedre vandmiljø. Af Schou, J.S. et al. 128 s.
624 Økologisk Risikovurdering af Genmodifi cerede Planter i 2006. Rapport over behandlede forsøgsudsætninger og markedsføringssager. Af Kjellsson, G. et al. 24 s.
623 The Danish Air Quality Monitoring Programme. Annual Summary for 2006. By Kemp, K. et al. 41 pp.
622 Interkalibrering af marine målemetoder 2006. Hjorth, M. et al. 65 s.
621 Evaluering af langtransportmodeller i NOVANA. Af Frohn, L.M. et al. 30 s.
620 Vurdering af anvendelse af SCR-katalysatorer på tunge køretøjer som virkemiddel til nedbringelse af NO2 forureningen i de største danske byer. Af Palmgren, F., Berkowicz, R., Ketzel, M. & Winther, M. 39 s.
619 DEVANO. Decentral Vand- og Naturovervågning. Af Bijl, L. van der, Boutrup, S. & Jensen, P.N. 35 s.
618 Strategic Environmental Impact Assessment of hydrocarbon activities in the Disko West area. By Mosbech, A., Boertmann, D. & Jespersen, M. 187 pp.
617 Elg i Danmark. Af Sunde, P. & Olesen, C.R. 49 s.
615 NOVANA. Det nationale program for overvågning af vandmiljøet og naturen. Programbeskrivelse 2007-09. Del 2. Af Bijl, L. van der, Boutrup, S. & Jensen, P.N. 119 s.
614 Environmental monitoring at the Nalunaq Gold Mine, South Greenland 2006. By Glahder, C.M. & Asmund, G. 26 pp.
613 PAH i muslinger fra indre danske farvande, 1998-2005. Niveauer, udvikling over tid og vurdering af mulige kilder. Af Hansen, A.B. 70 s.
612 Recipientundersøgelse ved grønlandske lossepladser. Af Asmun, G. 110 s.
611 Projection of Greenhouse Gas Emissions – 2005-2030. By Illerup, J.B. et al. 187 pp.
610 Modellering af fordampning af pesticider fra jord og planter efter sprøjtning. Af Sørensen, P.B. et al. 41 s.
609 OML : Review of a model formulation. By Rørdam, H., Berkowicz, R. & Løfstrøm, P. 128 pp.
608 PFAS og organotinforbindelser i punktkilder og det akvatiske miljø. NOVANA screeningsundersøgelse. Af Strand, J. et al. 49 s.
Nr./No. 2006
607 Miljøtilstand og udvikling i Viborgsøerne 1985-2005. Af Johansson, L.S. et al. 55 s.
606 Landsdækkende optælling af vandfugle, januar og februar 2004. Af Petersen, I.K. et al. 75 s.
605 Miljøundersøgelser ved Maarmorilik 2005. Af Johansen, P. et al. 101 s.
604 Annual Danish Emission Inventory Report to UNECE. Inventories from the base year of the protocols to year 2004. By Illerup, J.B. et al. 715 pp.
603 Analysing and synthesising European legislation in raletion to water. A watersketch Report under WP1. By Frederiksen, P. & Maenpaaa, M. 96 pp.
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National Environmental Research Institute ISBN 978-87-7073-003-7University of Aarhus . Denmark ISSN 1600-0048
This report is Denmark’s National Inventory Report reported to the Confe-rence of the Parties under the United Nations Framework Convention on Climate Change (UNFCCC) due by 15 April 2007. The report contains infor-mation on Denmark’s inventories for all years’ from 1990 to 2005 for CO2, CH4, N2O, HFCs, PFCs and SF6, CO, NMVOC, SO2.